ABOUT THE TEAM

The People Analytics team is at the forefront of transforming how we understand and optimize our workforce. We are a dynamic group of analytical thinkers and storytellers who leverage advanced analytics and robust methodologies to create strategic insights from complex HR data. Collaborating closely with HR Business Partners, Program Management Teams, Business Operations, Talent Acquisition and various cross functional stakeholders we provide actionable insights that drive informed decisions across the entire employee lifecycle; from optimizing talent acquisition and development to enhancing engagement and retention. Our work directly shapes a thriving employee experience, fuels organizational growth, and ensures our people strategy is truly data-driven.

ABOUT THE JOB

This role is central to our People Data & Analytics team, where you will be instrumental in building and maintaining the robust data infrastructure that powers our strategic insights. You'll own the full data lifecycle, from ensuring accurate ingestion and integration of diverse HR data sources, to designing, developing, and optimizing data models and pipelines. Your primary objective will be to transform raw, disparate information into clean, reliable, and analytics-ready datasets, empowering our People Analysts and business stakeholders to unlock deeper understanding of our workforce, enhance employee experience, and drive data-driven decision-making. Success in this role requires a strong technical foundation, meticulous attention to data quality, and a passion for crafting efficient data solutions.

WHAT YOU'LL DO

• Design, build, and optimize robust ETL/ELT pipelines to reliably ingest, integrate, and transform diverse people data from various HR systems (HRIS, ATS, LMS, etc.) into our data platform.
• Develop, maintain, and govern scalable and secure data models, schemas, and ontologies specifically for people analytics, ensuring data quality, consistency, and accessibility for downstream consumption.
• Contribute to the strategic design, development, and evolution of our people data platform and tooling, advocating for engineering best practices, automation, and a scalable analytics ecosystem (e.g., leveraging SQLMesh, Iceberg, Flyte).
• Partner closely with People Analysts, HR Business Partners, and other stakeholders to understand their analytical needs and translate them into robust data solutions, providing well-structured, documented, and reliable datasets.
• Implement and monitor data quality checks, identify discrepancies, troubleshoot data issues, and ensure the reliability and integrity of people data across all systems.
• Continuously monitor the performance of data pipelines and models, identifying bottlenecks and implementing solutions to ensure the efficiency and scalability of our people data infrastructure.
• Create and maintain comprehensive documentation for data pipelines, models, and processes, and champion data engineering best practices (e.g., version control, testing, CI/CD) within the team.
• Implement and enforce strict data security measures and ensure all data handling practices comply with internal policies and external regulations (e.g., GDPR, CCPA) related to employee data privacy.
• Collaborate with broader enterprise analytics and data engineering teams to align on data architecture standards, integrate people data with other business domains, and contribute to the overall evolution of the company's data platform.

REQUIRED QUALIFICATIONS

• 5-7 years of progressive experience in Data Engineering, Analytics Engineering, or a similar role focused on building and optimizing data pipelines and data infrastructure.
• Expert-level proficiency in SQL for complex data manipulation and querying, and advanced Python for scripting, data processing, and automation.
• Extensive experience with cloud-based data warehousing solutions (e.g., Snowflake, Google BigQuery, AWS Redshift, Databricks/Delta Lake) and data lake technologies (e.g., AWS S3, Azure Data Lake Storage).
• Deep understanding and proven experience in designing, implementing, and maintaining robust data models (e.g., dimensional modeling, Kimball methodology) for analytical purposes.
• Hands-on experience building and optimizing complex ETL/ELT processes and data pipelines using modern tools such as dbt, Apache Airflow, Flyte, Dagster, or similar orchestration platforms.
• Excellent communication skills, both written and verbal, with the ability to translate technical concepts for non-technical stakeholders and collaborate effectively across diverse teams.
• Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related field.
• Strong technical foundation in data engineering, with expertise in SQL, Python, and experience working with cloud-based data platforms (e.g., AWS, GCP, Azure).

Preferred Qualifications:

• Experience with big data processing frameworks (e.g., Apache Spark, Flink) and advanced data warehousing features like schema evolution or time travel (e.g., Apache Iceberg, Delta Lake).
• Direct hands-on experience with tools mentioned in our stack like Palantir Foundry, SQLMesh, Flyte, or similar cutting-edge data orchestration and transformation platforms.
• Relevant professional certifications from major cloud providers (e.g., AWS Certified Data Analytics - Specialty, Google Cloud Professional Data Engineer).
• Experience with tools like Terraform, CloudFormation, Docker, or Kubernetes for managing data infrastructure and deploying applications.
• Familiarity with integrating data pipelines with leading BI tools (e.g., Tableau, Power BI, Looker) to optimize dashboard performance and data accessibility for end-users.
• Deep expertise working with data from specific enterprise HRIS systems like Rippling, Workday, and Oracle HCM Cloud including their data models and APIs.
• Solid understanding of HR data concepts, metrics, and common HR systems (HRIS, ATS, LMS), with a strong interest in People Analytics.

About the role

Starburst is looking for an experienced Staff Software Engineer to sit at the intersection of software engineering and data engineering — building the internal platforms, pipelines, and data products that power how we understand our customers and our business. You'll work across Starburst Enterprise and Starburst Galaxy, using our own technology to solve real problems at scale, while also contributing software that other teams depend on.

Because data engineers are one of our primary target personas, you'll bring a uniquely valuable perspective: you'll use our products daily and feed that experience directly back into how we build and improve them. You'll work closely with a dynamic, talented, globally distributed team, writing production-quality software alongside building reliable data systems — with a consistent emphasis on quality, maintainability, and speed to insight.

As a Staff Software Engineer at Starburst you will:

• Design, build, and operate a high-quality data lake and the software systems that feed it, supporting product management, finance, customer support, and engineering
• Write production-grade software — services, APIs, tooling, and automation — that enables data workflows and integrations at scale
• Find innovative ways to use Trino and Starburst to solve complex data management and software architecture challenges
• Collaborate with technical leads, product managers, and data analysts to build robust data products and analytics
• Leverage AI to democratize access to datasets for users throughout Starburst, including building and maintaining the software infrastructure that makes this possible
• Enable dataset preparation and model evaluation for Starburst's AI projects
• Define and evolve engineering processes and best practices that span both software and data domains, with a focus on reliability and actionable results
• Work closely with leaders across teams to iterate on data architecture and the software systems that implement it, keeping quality high at every layer
• Identify emerging patterns in data management and software engineering, and work with Starburst's product teams to bring those innovations to market

Some of the things we look for

• A Trino enthusiast looking to push the bounds in an AI future
• At least 7 years of combined experience in software engineering and/or data engineering, with genuine depth in both
• Strong software engineering fundamentals: writing clean, testable, well-reviewed code in Java, Python, or Scala
• Experience building and optimizing data pipelines using Trino, Spark, dbt, and related frameworks
• Experience designing and building backend services, APIs, and data platform components
• Experience managing data and application infrastructure in public clouds, particularly AWS (EMR, EC2, S3, IAM); multi-cloud experience (GCP/Azure) is a plus
• Experience using and managing orchestration frameworks such as Apache Airflow or Dagster
• Knowledge of RAG and other design patterns for AI-powered applications
• Fluency in SQL and comfort moving between SQL and general-purpose programming as the task demands
• Experience building API integrations for extracting data from third-party sources
• Knowledge of data modeling techniques appropriate for modern data lakes
• Proficiency with Configuration-as-Code and Infrastructure-as-Code tools such as Pulumi, Terraform, and/or Ansible
• A track record of shipping reliable software and data systems on time, with disciplined practices around testing, code quality, and review
• Strong written and verbal communication skills, with the organizational ability to coordinate across teams

Where could this role be based?

• San Francisco, US

Position Overview:

We’re looking for a Senior Data Implementation Engineer to help lead and accelerate Acadian’s data transformation efforts. This role is central to reimagining and rebuilding our data platform so that we can continue to scale our investment research, production data workflows, and operational capabilities in a rapidly evolving landscape.

You’ll be hands-on in designing and implementing modern data pipelines and platform capabilities using tools such as Apache Arrow, Polars, Dagster, and other emerging technologies. You’ll also help shape how we use AI-assisted development tools, such as Claude & ChatGPT, and related automation approaches, to improve the speed, quality, and maintainability of data pipeline development.

This is a senior engineering role for someone who can operate across both strategy and execution: deeply understanding existing systems, designing pragmatic transition paths, mentoring others, and building reliable, scalable, high-performance data infrastructure that directly supports Acadian’s quantitative investment process.

Acadian supports a hybrid work environment; employees are on-site in the Boston office 3 days a week.

What You’ll Do

• Lead Data Pipeline Design and Implementation
  • Design, build, and optimize robust data pipelines that are reliable, scalable, observable, and performant. Use modern data engineering tools and patterns to support investment research, production workflows, and downstream analytics.
• Drive System Modernization
  • Deeply understand existing data systems, identify architectural limitations, and lead the transition toward more modern, maintainable, and efficient data architectures.
• Shape Technical Direction
  • Partner with Data Platform Engineering, Data Analysis, and investment teams to define implementation patterns, pipeline standards, and architectural approaches that can scale across the organization.
• Apply AI Tooling to Engineering Workflows
  • Use AI-assisted development tools such as Claude, ChatGPT, or similar technologies to accelerate pipeline development, automate repetitive workflows, improve documentation, generate tests, and enhance developer productivity.
• Mentor and Lead Others
  • Provide technical guidance to junior and mid-level engineers. Help raise the engineering bar through code reviews, design discussions, pairing, documentation, and thoughtful mentorship. Lead small project teams or workstreams when needed.
• Improve Reliability and Operational Excellence
  • Develop systems with strong testing, monitoring, lineage, data quality checks, and operational supportability. Help ensure that data pipelines are resilient, transparent, and easy to maintain.
• Collaborate Across Teams
  • Work closely with stakeholders across technology, data, research, and investment teams to understand requirements, resolve ambiguity, and deliver solutions that create measurable business value.
• Continuously Improve the Platform
  • Identify opportunities to simplify, automate, and improve the data ecosystem. Champion practical innovation while balancing speed, stability, maintainability, and long-term architectural quality.

We’re Looking for Teammates With

• Bachelor’s degree in a relevant field

• A strong background in data engineering, data platform implementation, or data-intensive software engineering, ideally with 5+ years of experience in a financial, investment, or technology-driven environment.
• Deep experience building production-grade data pipelines, including ingestion, transformation, validation, orchestration, monitoring, and deployment.
• Strong Python programming skills, with relevant experience working with Dagster, Airflow or similar technologies.
• Experience with modern data architecture patterns such as data lakehouse, Medallion architecture, composable data systems, microservices, and event-driven or workflow-oriented architectures.
• Familiarity with Data Mesh concepts, including decentralized data ownership, domain-oriented data products, governance, and discoverability.
• Experience with orchestration, workflow, and data transformation tools such as dbt, Polars, Pandas, Apache Arrow, or comparable technologies.
• Practical experience using AI-assisted development tools such as Claude, ChatGPT, GitHub Copilot, or similar platforms to support data pipeline development, automation, testing, documentation, and engineering productivity.
• Demonstrated ability to mentor engineers, lead technical workstreams, and influence engineering practices across a team.
• A strong sense of ownership and the ability to operate effectively in ambiguous environments, balancing tactical delivery with long-term architectural thinking.
• A passion for building systems that are efficient, scalable, reliable, observable, and easy for others to use.
• An understanding of how data infrastructure supports the broader investment process, and a desire to deliver solutions that improve outcomes for clients and create meaningful business impact.

The base salary range for this role is $175,000 – $200,000 per year. Actual compensation will be determined based on a candidate’s skills, qualifications, and relevant experience. In addition to base pay, this position may be eligible for discretionary incentive compensation and includes participation in Acadian’s comprehensive benefits program, which includes health, retirement, and wellness offerings.

Software Engineer II - Analytics Data Engineering | AI & Analytics Data Enablement

Location: Boston, MA

About the Role

Data is the lifeblood of Klaviyo. As a Data Engineer on this team, you will sit at the intersection of infrastructure and intelligence. You won’t just be moving data; you’ll be building the foundations that power our next generation of AI-driven features and enterprise-scale analytics.

You will bridge the gap between data engineering and product impact. Your work will involve developing scalable Spark pipelines, tuning our storage and query patterns to ensure low-latency performance for our enterprise customers, and modeling the high-impact datasets that drive Klaviyo's analytics and machine learning engines.

What You’ll Do

• Build Production-Grade Foundations: Develop and maintain scalable data pipelines and core tables using PySpark, Airflow, and dbt. You will implement the foundational datasets that power our AI, ML, and Analytics products.
• Optimize for Enterprise Performance: Tune Spark jobs and storage patterns to ensure low-latency data retrieval. You will help implement materialized views and efficient partitioning strategies to support high-performance reporting at scale.
• Treat Data as a Product: Contribute to the full lifecycle of datasets. This includes defining clear data contracts with upstream teams, writing maintainable code via peer reviews, and ensuring every asset is well-documented and trusted by downstream users.
• Drive Operational Excellence: Ensure the reliability of our data engine by monitoring for freshness, volume anomalies, and schema changes. You will be responsible for ensuring that when a customer loads a dashboard, the data is accurate and on time.
• Partner Cross-Functionally: Collaborate with Product, Engineering, and AI/ML teams to define consistent metrics that align with business goals. You will act as a bridge to ensure new features land with robust data support.
• Innovate with AI: Look for opportunities to put AI at the center of your workflow, whether it is using AI to generate tests, detect data anomalies, or accelerate complex analysis.

Who You Are

• The Experience: 2+ years of experience in data engineering or a data-intensive software engineering role. You’ve moved past the "beginner" phase and are comfortable taking a project from a design doc to a production deployment.
• Fluent in SQL and Python: You have a solid grasp of SQL for high-performance querying and are comfortable using Python for data manipulation and automation. You focus on writing code that balances speed with reliability.
• Distributed Systems Knowledge: You have hands-on experience with Spark (PySpark/SparkSQL) and understand how to tune jobs for performance in a cloud environment (AWS/EMR).
• Modeling Intuition: You understand the difference between a "raw table" and a "semantic layer." You’ve worked with modern modeling tools (like dbt) and understand partitioning, schema evolution, and lakehouse concepts (Iceberg/Delta).
• Performance Minded: You care about latency. You enjoy the challenge of making a query run faster and understand how to use materialized views and caching effectively.
• Collaborative & Curious: You’re an inclusive collaborator who enjoys working with Product and Data Science. You’re excited to experiment with AI tools to make your own engineering workflow more efficient.

Nice to Haves

• Experience with Iceberg table maintenance and compaction.
• Exposure to Terraform or other Infrastructure-as-Code tools.
• A background in Martech or SaaS platforms dealing with high-frequency event data.
• Experience building data products that directly power customer-facing UI components and/or support AI/ML features.
• Experience building near real-time or streaming pipelines for user-facing analytics or monitoring.
• Hands-on work with analytics engineering tools and practices (e.g., dbt, metrics layers, semantic models).
• Familiarity with statistical modeling and machine learning.

Klaviyo is growing fast and we have openings for all skill levels across all of our teams. Learn more about our engineering culture at https://klaviyo.tech

We love tackling tough engineering problems and look for Engineers who specialize in certain areas but are passionate about building, owning & scaling features end to end from scratch and breaking through any obstacle or technical challenge in their way. We push each other to move out of our comfort zone, learn new technologies and work hard to ensure each day is better than the last.

Klaviyo operates a real-time data platform built for massive scale on Amazon Web Services (AWS). Engineers come to Klaviyo with experience in a variety of languages and from a number of disciplines.

Responsibilities

The teams in the Data Platform area work with large scale distributed systems and databases to power core Klaviyo functionality and generate data-driven insights. As an engineer in this area, you will be responsible for designing, building, and maintaining systems that support other engineering teams across Klaviyo. You will work with a cross functional team to help design systems, write high quality code, create infrastructure, and operate large scale systems in a production environment.

Team Tech Stack

• AWS
• Python
• Kubernetes
• MySQL
• Clickhouse
• Kafka
• Spark
• Flink
• Iceberg
• Terraform
• Airflow

Required Skills

• Degree or equivalent experience in a software engineering discipline
• 2+ years of experience in a software engineering discipline
• Proficient in using at least one modern programming language
• Experience designing, building, and operating distributed systems in a cloud computing environment
• Experience with OLTP or OLAP databases
• Experience with high volume data processing and storage systems
• Experience provisioning infrastructure using Terraform or a similar infrastructure as code tool
• Experience using Linux operating systems
• Able to communicate well
• Able to work with others on a team
• Able to learn continuously and adapt to a high growth environment
• Able to stick with and solve difficult problems

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

Klaviyo is growing fast and we have openings for all skill levels across all of our teams. Learn more about our engineering culture at https://klaviyo.tech

We love tackling tough engineering problems and look for Engineers who specialize in certain areas but are passionate about building, owning & scaling features end to end from scratch and breaking through any obstacle or technical challenge in their way. We push each other to move out of our comfort zone, learn new technologies and work hard to ensure each day is better than the last.

Klaviyo operates a real-time data platform built for massive scale on Amazon Web Services (AWS). Engineers come to Klaviyo with experience in a variety of languages and from a number of disciplines.

Responsibilities

The Data Automation team builds tooling that helps our Data Platform engineers provision and test infrastructure to power core Klaviyo functionality and generate data-driven insights. As an engineer on this team, you will be responsible for designing, building, and maintaining systems that help us operate our mission critical analytics infrastructure. You will work with a cross functional team to help design systems, create infrastructure, write high quality code, and operate large scale systems in a production environment.

Team Tech Stack

• AWS
• Python
• Terraform
• Kubernetes
• Clickhouse
• DynamoDB
• MySQL
• Kafka
• Spark
• Flink
• Iceberg
• Airflow

Required Skills

• Degree or equivalent experience in a software engineering discipline
• Proficient in using at least one modern programming language
• Able to communicate well
• Able to work with others on a team
• Able to learn continuously and adapt to a high growth environment
• Able to stick with and solve difficult problems
• You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient.

Preferred Skills

• Experience designing, building, and operating distributed systems in a cloud computing environment
• Experience provisioning infrastructure using Terraform or a similar infrastructure as code tool
• Experience performance tuning and scaling systems
• Experience with OLTP and OLAP databases
• Experience with high volume data processing and storage systems
• Experience using Linux operating systems

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

Mission Summary:

As a Staff Engineer within the Platform Engineering team, you will be a hands-on technical leader driving the evolution of Motional's global cloud and hybrid infrastructure. Operating as a senior individual contributor, you'll tackle our hardest platform problems directly — designing systems, creating code, and setting technical direction for initiatives that span multiple teams. Your work will help transform our R&D environment into a hardened, cost-efficient, and scalable commercial backbone, and ensure our internal platforms deliver the developer velocity required for rapid AV deployment.

What you'll be doing:

• Design and build major components of our cloud architecture — including networking, storage, compute abstraction layers. You'll own the technical design end-to-end and create code where it matters most
• Drive technical evolution of our Kubernetes-based developer platform. Identify the highest-leverage improvements to scalability, cost attribution, access control, and developer ergonomics — then design and implement them to reduce configuration complexity and accelerate engineering velocity
• Raise the reliability bar across Platform Engineering. Define and implement standards for SLIs, SLOs, observability, and failover, and partner with peer teams to roll them out across platform-layer services
• Drive FinOps practices — cost visibility, attribution, accountability as well as optimization recommendations
• Design and ship AI-native developer experiences by integrating LLM-powered tooling into CI, build systems, monitoring platforms, and local development workflows to remove friction and boost engineering throughput
  
  

What we're looking for:

• Bachelor's or Master's in Computer Science, Engineering, or a related field (or equivalent practical experience)
• 9 years in Software or Infrastructure Engineering, with significant experience in a senior IC capacity
• Deep, expert-level knowledge of Kubernetes internals and ecosystem (Operators (Crossplane, ArgoWorkflow/Airflow, KubeRay), Kubecost/OpenCost, Service Mesh, GitOps and other CNCF projects)
• Proven track record of designing or migrating complex workloads between on-prem data centers and major cloud providers (AWS, GCP, other)
• Experience with Infrastructure-as-Code (Terraform/CloudFormation, Terraform Cloud/Spacelift/Atlantis) and advanced observability tooling (OpenTelemetry, Prometheus/VictoriaMetrics, Grafana, Datadog)
• Expert in architecting AI-driven operations, leveraging LLMs and agentic workflows to automate incident response, predictive scaling, and intelligent system observability
• Demonstrated ability to influence technical roadmaps across an entire engineering organization and mentor senior technical staff
  
  

Bonus points (not required):

• Proven expertise in CI/CD orchestration, build systems (Bazel, RBE), frameworks
• Prior experience with large-scale machine learning training pipelines
• Experience leading technical projects in a globally distributed team
• Open-source contributions to Kubernetes, observability, or infrastructure tooling ecosystems

Mission Summary:

As a Staff Engineer within the Platform Engineering team, you will be a hands-on technical leader driving the evolution of Motional's global cloud and hybrid infrastructure. Operating as a senior individual contributor, you'll tackle our hardest platform problems directly — designing systems, creating code, and setting technical direction for initiatives that span multiple teams. Your work will help transform our R&D environment into a hardened, cost-efficient, and scalable commercial backbone, and ensure our internal platforms deliver the developer velocity required for rapid AV deployment.

What you'll be doing:

• Design and build major components of our cloud architecture — including networking, storage, compute abstraction layers. You'll own the technical design end-to-end and create code where it matters most
• Drive technical evolution of our Kubernetes-based developer platform. Identify the highest-leverage improvements to scalability, cost attribution, access control, and developer ergonomics — then design and implement them to reduce configuration complexity and accelerate engineering velocity
• Raise the reliability bar across Platform Engineering. Define and implement standards for SLIs, SLOs, observability, and failover, and partner with peer teams to roll them out across platform-layer services
• Drive FinOps practices — cost visibility, attribution, accountability as well as optimization recommendations
• Design and ship AI-native developer experiences by integrating LLM-powered tooling into CI, build systems, monitoring platforms, and local development workflows to remove friction and boost engineering throughput
  
  

What we're looking for:

• Bachelor's or Master's in Computer Science, Engineering, or a related field (or equivalent practical experience)
• 9 years in Software or Infrastructure Engineering, with significant experience in a senior IC capacity
• Deep, expert-level knowledge of Kubernetes internals and ecosystem (Operators (Crossplane, ArgoWorkflow/Airflow, KubeRay), Kubecost/OpenCost, Service Mesh, GitOps and other CNCF projects)
• Proven track record of designing or migrating complex workloads between on-prem data centers and major cloud providers (AWS, GCP, other)
• Experience with Infrastructure-as-Code (Terraform/CloudFormation, Terraform Cloud/Spacelift/Atlantis) and advanced observability tooling (OpenTelemetry, Prometheus/VictoriaMetrics, Grafana, Datadog)
• Expert in architecting AI-driven operations, leveraging LLMs and agentic workflows to automate incident response, predictive scaling, and intelligent system observability
• Demonstrated ability to influence technical roadmaps across an entire engineering organization and mentor senior technical staff
  
  

Bonus points (not required):

• Proven expertise in CI/CD orchestration, build systems (Bazel, RBE), frameworks
• Prior experience with large-scale machine learning training pipelines
• Experience leading technical projects in a globally distributed team
• Open-source contributions to Kubernetes, observability, or infrastructure tooling ecosystems

Mission Summary:

As a Staff Engineer within the Platform Engineering team, you will be a hands-on technical leader driving the evolution of Motional's global cloud and hybrid infrastructure. Operating as a senior individual contributor, you'll tackle our hardest platform problems directly — designing systems, creating code, and setting technical direction for initiatives that span multiple teams. Your work will help transform our R&D environment into a hardened, cost-efficient, and scalable commercial backbone, and ensure our internal platforms deliver the developer velocity required for rapid AV deployment.

What you'll be doing:

• Design and build major components of our cloud architecture — including networking, storage, compute abstraction layers. You'll own the technical design end-to-end and create code where it matters most
• Drive technical evolution of our Kubernetes-based developer platform. Identify the highest-leverage improvements to scalability, cost attribution, access control, and developer ergonomics — then design and implement them to reduce configuration complexity and accelerate engineering velocity
• Raise the reliability bar across Platform Engineering. Define and implement standards for SLIs, SLOs, observability, and failover, and partner with peer teams to roll them out across platform-layer services
• Drive FinOps practices — cost visibility, attribution, accountability as well as optimization recommendations
• Design and ship AI-native developer experiences by integrating LLM-powered tooling into CI, build systems, monitoring platforms, and local development workflows to remove friction and boost engineering throughput
  
  

What we're looking for:

• Bachelor's or Master's in Computer Science, Engineering, or a related field (or equivalent practical experience)
• 9 years in Software or Infrastructure Engineering, with significant experience in a senior IC capacity
• Deep, expert-level knowledge of Kubernetes internals and ecosystem (Operators (Crossplane, ArgoWorkflow/Airflow, KubeRay), Kubecost/OpenCost, Service Mesh, GitOps and other CNCF projects)
• Proven track record of designing or migrating complex workloads between on-prem data centers and major cloud providers (AWS, GCP, other)
• Experience with Infrastructure-as-Code (Terraform/CloudFormation, Terraform Cloud/Spacelift/Atlantis) and advanced observability tooling (OpenTelemetry, Prometheus/VictoriaMetrics, Grafana, Datadog)
• Expert in architecting AI-driven operations, leveraging LLMs and agentic workflows to automate incident response, predictive scaling, and intelligent system observability
• Demonstrated ability to influence technical roadmaps across an entire engineering organization and mentor senior technical staff
  
  

Bonus points (not required):

• Proven expertise in CI/CD orchestration, build systems (Bazel, RBE), frameworks
• Prior experience with large-scale machine learning training pipelines
• Experience leading technical projects in a globally distributed team
• Open-source contributions to Kubernetes, observability, or infrastructure tooling ecosystems

We are looking for a Staff Security Software Engineer who is passionate about detecting and mitigating abuse in the Cloud. 

As a Staff Security Software Engineer at DigitalOcean, you will join a dynamic team dedicated to revolutionizing cloud computing and AI. This role reports to the Senior Manager of Security Engineering within the Security organization. You should have experience developing and maintaining software systems in a complex, high-scale environment, analyzing outcomes and communicating insights to stakeholders, and rapidly devising solutions that are in alignment with overall business goals. Our team is focused on enabling DigitalOcean to scale safely by building systems that detect and prevent abuse on our platform. We highly value collaboration and growth, and work closely with other teams in the Security organization to deliver high quality solutions and also build tooling to boost their efficiency.

Our customers trust us with their data and operations, and we take that responsibility seriously. Security at DO means solving highly complex problems on a large scale that have real impact for our customers, our products, and for the larger internet community.

What You’ll Do:

• Design, develop, and deploy resilient services and tooling that provide security capabilities at scale, primarily in Go, with some work in Javascript and Python.
• Research and design automated approaches to detecting and actioning abuse and misuse of DigitalOcean products and services.
• Work with product and engineering teams to design and implement secure architectures.
• Design and build internal tools that enable our Security Operations Center to do more, faster.
• Measure the efficacy of our services and tools, using logs, metrics, and audit records, to drive reporting and decision-making, leading to iterative and transformative security improvements as DigitalOcean scales. All while being mindful that security should be an efficiency enabler for the business, not an obstacle.

What You’ll Add to DigitalOcean:

• 8+ years of hands-on experience in software engineering projects, ideally with a security focus, in a complex, high-scale SaaS or IaaS environment. You are an expert in writing robust code with good test coverage and demonstrated success in delivering projects.
• Understanding of cloud-native services and infrastructure provisioning (e.g. compute, storage, networking).
• Deep familiarity with cloud services & infrastructure—compute, storage, network, managed services—and related abuse tactics, including DDoS, spamming, phishing, cryptomining, and bot networks.
• Strong analytical and reporting skills, with proficiency in SQL and data warehouse querying (e.g., Snowflake, Redshift, BigQuery)
• A customer-first mindset—you aim to maximize abuse prevention while avoiding false-positives that could impact high-value customers.
• Excellent communication and documentation skills; you can clearly articulate patterns, mitigation strategies, and tradeoffs to both technical and non-technical stakeholders. You have a record of partnering with internal engineering teams and non-technical stakeholders to gather requirements and tackle security problems across an entire stack with empathy and creativity.
• High integrity, strong judgment, and a track record of working independently on high-impact investigations and policy decisions.

Bonus qualifications:

• Experience with microservice architectures, asynchronous and event-driven processing, and synchronous gRPC/HTTP-based requests.
• Experience in building secure by default services and applications.
• Familiarity with technologies such as gRPC, Docker, Elasticsearch, Prometheus/VictoriaMetrics, Kubernetes, and GitHub Actions.
• Experience with Looker, Snowflake, dbt, and Airflow

Compensation Range: 

• $166,400 - $208,000

*This is a remote role

#LI-Remote

We are building an AI-native Data Cloud and need a VP of Engineering who owns architecture, execution, and business outcomes end-to-end. The buck stops with this role for platform reliability, developer experience, and Data Cloud adoption.

DigitalOcean’s Data Cloud will power AI applications end-to-end—from data ingestion to real-time inference—enabling developers to build production-grade RAG systems, AI agents, and analytics workflows on a single, integrated platform. This role is responsible for delivering that platform by leveraging and extending DigitalOcean’s existing infrastructure, PaaS, and AI capabilities.

This is not a traditional data platform role. You will define and execute the strategy to move DigitalOcean up the stack—from infrastructure provider to a differentiated AI application and inference platform. Success will be measured through roadmap velocity, system reliability, developer adoption, and measurable business impact.

We are looking for a leader who translates strategy into shipped systems, with clear accountability for outcomes.

What You’ll Do:

Data Cloud Platform

Architecture and delivery of a unified platform spanning:

• Ingestion (connectors, CDC, streaming)
• Storage (object, OLAP, vector, graph)
• Processing (ELT pipelines, real-time and batch)
• Query & analytics (SQL, hybrid search, RAG)
• Governance (catalog, lineage, versioning, access control)

AI-Native Capabilities

• Knowledgebase-as-a-Service (RAG pipelines)
• Vector and hybrid retrieval systems
• Feature and embedding pipelines
• Real-time inference data paths

Product & Execution

• Deliver GA-grade services with strong SLAs, cost efficiency, and simplicity
• Drive roadmap aligned to Digital Native Enterprise (DNE) customers
• Establish and manage clear metrics: adoption, retention, ARPU, performance, reliability

Organizational Leadership

• Build and scale a high-performing global organization (US and India)
• Implement single-threaded ownership (STO) across services
• Partner deeply with Product, GTM, and Platform Engineering

What Success Looks Like (12 months)

• Launch a cohesive Data Cloud platform—not a collection of fragmented services
• Achieve meaningful adoption across AI-native workloads (RAG, inference, analytics)
• Reduce developer time-to-value from weeks to hours
• Establish clear differentiation versus hyperscalers on simplicity and cost

What You’ll Bring:

• Proven track record of building and operating cloud-scale data platforms (OLAP, lakehouse, streaming, or ML systems), with strong architectural judgment across storage, processing, and query layers
• Experience integrating open-source ecosystems (e.g., Spark, Airflow, dbt) into managed cloud services at scale
• Experience with AI/ML data pipelines (feature stores, embeddings, RAG)
• Demonstrated ability to operate in ambiguity, make high-quality decisions with incomplete data, and drive multi-quarter execution with measurable outcomes
• Proven experience scaling engineering organizations and delivering under real-world constraints

Critical requirement:
You have personally driven complex distributed systems from concept to production—not just managed teams that did.

Why This Role Matters

The Data Cloud is the control plane for AI applications. If DigitalOcean gets this right, it becomes the platform where developers build and scale intelligent applications—not just where infrastructure runs. If not, it remains an interchangeable infrastructure.

Compensation Range: 

• $279,200.00 - $349,000

*This is a remote role

JR: 2026-7683

#LI-Remote

At Klaviyo, we love tackling tough engineering problems and look for engineers who specialize in certain areas but are passionate about building, owning & scaling features end to end from scratch and breaking through any obstacle or technical challenge in their way. We push each other to move out of our comfort zone, learn new technologies and work hard to ensure each day is better than the last.

Team Overview:

Klaviyo operates a real-time data analytics platform written primarily in Python that is built for massive scale and hosted on Amazon Web Services (AWS). The Data Automation team builds tooling and establishes best practices and technology choices that help enable the Data Platform area to power core Klaviyo functionality and generate data-driven insights.

As a founding member on the Data Automation team, you will have ownership over defining the technical and organizational vision for the team. You will be responsible for leading the team to design and build systems that help us operate our mission critical analytics infrastructure.

Team Tech Stack:

• AWS
• Terraform
• Python
• Kubernetes
• Clickhouse
• DynamoDB
• MySQL
• Kafka
• Spark
• Flink
• Airflow

How You'll Make a Difference

• You will be responsible for building and managing a team of 7+ engineers to fulfill your data automation vision and roadmap.
• Responsible for coaching engineers, managing/reviewing technical documentation and articulating a phased approach to achieving the team's overall technical vision.
• Be responsible for the mission, technical direction and operations of projects that have immediate impact.
• Wholly owning the frameworks that enable us to provision, update, test, monitor, and scale infrastructure to power features that 183,000+ customers rely on daily to reach 4B+ consumers to drive their business forward.
• Optimizing infrastructure with the teams that own Klaviyo’s analytics system that provides insight on hundreds of terabytes of data.
• Transform workflows by putting AI at the center, building smarter systems and ways of working from the ground up.

Required Skills

• BA or BS Degree in Computer Science, related field, or equivalent experience
• 8+ years of software engineering experience in a DevOps environment with specific experience in data processing systems.
• 3+ years of people management experience
• Hands-on experience designing, building, and operating reliable, fault-tolerant, and high performance distributed systems
• Ability to handle yourself in outage situations and to drive failures to root cause analysis and prevention of future issues
• Experience writing code using best practices in a language such as Python, Ruby, Go, etc.
• You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient.
• Proven ability to come up to speed quickly
• Hybrid office work 3x/week out of our Boston office

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

Senior Software Engineer, AI & Analytics Data Enablement

Location: Boston, MA

As a Senior Software Engineer on Klaviyo’s AI & Analytics Data Enablement team, you’ll build and own the data foundations that power our Analytics & AI engine. You’ll design resilient pipelines and analytics models that turn billions of events and domain objects into trusted, actionable insights for Analytics and AI product features. “Your work will help our customers understand the performance of their marketing and power AI & Analytics experiences for our customers.”

About the Role

You’ll join a cross-functional R&D team focused on the availability, quality, and usability of AI and Analytics data for our customers. In this Senior-level role, you’ll lead complex software and data engineering projects across multi-component systems, from shaping requirements to design, implementation, and long-term operation. You’ll be a go-to expert for AI and analytics data, collaborating with product managers, AI, analytics, and other engineering teams to ensure our data is accurate, observable, and ready to drive insights and enable AI/ML products at scale.

You’ll also help evolve our technical standards for data engineering, raising the bar on data modeling, performance, and reliability, while mentoring other engineers on best practices and modern data tooling.

What You’ll Do

Lead the design and implementation of robust, scalable data pipelines and core tables for Klaviyo. These pipelines are crucial for powering customer facing analytics features and powering AI models to unlock the next stage of Klaviyo's products

Partner with Product, Engineering, and AI/ML teams to define clear, consistent metrics and data contracts that align to business goals and product outcomes.

Transform workflows by putting AI at the center, building smarter systems and ways of working from the ground up for example, using AI to generate tests, detect anomalies, summarize data issues, or accelerate analysis.

Mentor other engineers in data engineering best practices, code quality, observability, and system design; contribute to shared libraries, patterns, and tooling that improve developer velocity across R&D.

Who You Are

6+ years of software engineering with at least 4 or those being data engineering experience, with a strong track record building and operating data pipelines and analytics models in production.

Deep proficiency in large-scale SQL and data modeling for analytics. 

Strong programming skills in a modern language commonly used for data engineering (e.g. Python), and familiarity with orchestration and transformation tooling (e.g., Airflow, EMR, Spark/pyspark, or equivalents).

Experience working with modern data platforms (e.g., columnar data warehouses, object storage) and processing frameworks (batch and/or streaming).

Demonstrated ability to lead complex projects spanning multiple components and engineers—driving clarity, making tradeoffs, and delivering measurable impact on reliability, performance, or business outcomes.

Comfortable owning systems in production: you think about observability, incident response, incremental rollouts, and long-term maintainability as part of the design.

Excellent communication skills; you can explain data tradeoffs and constraints to non-technical partners and influence decisions with clear reasoning.

You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient.

Inclusive, low-ego collaborator who values diverse perspectives and is motivated by helping the team succeed, not just shipping your own code.

Nice to Haves

Experience in a product-led SaaS environment with large-scale event data

Experience in a customer facing data engineering role supports AI/ML features and Analytics Dashboards.

Hands-on work with analytics engineering tools and practices (e.g., dbt, metrics layers, semantic models).

Experience building near real-time or streaming pipelines for user-facing analytics or monitoring.

Domain experience in martech, marketing automation, or customer engagement platforms.

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

Beacon Dx is on a mission to transform clinical diagnostics for sleep and brain health. We are building the leading home-based platform for precision diagnostics, combining EEG and cardiopulmonary signals to deliver comprehensive, reimbursable assessments for sleep and central nervous system disorders. Our solution integrates multi-channel physiological data—including airflow, respiratory effort, oxygen saturation, and brain activity—into a single streamlined workflow powered by AI-enabled analytics. With a dedication to patient experience and a foundation in FDA 510(k)-cleared technology, Beacon Dx enables high-quality diagnostics at scale. We are redefining how patients are evaluated and managed for conditions that affect brain and sleep physiology—bringing clinical-grade insights directly to the home.

The Regional Sales Manager will sit within the Beacon Dx commercial organization and will be responsible for driving adoption of Beacon HST across the Northeast. This role reports directly to the National Vice President of Clinical Sales and does not initially manage direct reports. The role owns a high-priority territory that includes Pennsylvania, New York, New Jersey, Connecticut, Massachusetts, Maryland, Rhode Island, New Hampshire, Vermont, and Maine. Preferred candidate locations include New York, New Jersey, Massachusetts, or Pennsylvania, with preference for major metro access to support field travel and customer engagement.

Beacon Dx is building the next generation of home-based diagnostics by combining EEG and cardiopulmonary signals into a unified platform. This is not a transactional device role. Success requires the ability to sell into complex clinical environments, influence diagnostic pathways, and establish Beacon as a standard of care alternative to legacy HST solutions. The right candidate will convert clinical credibility into commercial scale by opening new accounts, expanding strategic accounts, displacing incumbent solutions, and partnering cross-functionally to ensure utilization after the sale.

What success looks like:

• Own full Northeast territory performance, including pipeline creation, deal progression, revenue attainment, and forecast accuracy.
• Drive new business and expand strategic accounts across sleep labs, IDTFs, health systems, and multi-site clinical organizations.
• Lead with clinical differentiation by translating EEG-based diagnostics, workflow advantages, and data quality into clear value propositions for physicians, administrators, and technical stakeholders.
• Maintain disciplined CRM hygiene in Zoho, including timely follow-up, clean opportunity staging, accurate close dates, and reliable revenue forecasting.
• Develop relationships with sleep physicians, pulmonologists, neurologists, technical directors, administrators, and executive decision-makers across complex buying committees.
• Execute high-impact product demonstrations and support clinical validation of Beacon HST in real-world customer environments.
• Attend regional and national conferences to generate leads, strengthen relationships, identify KOLs, and support market education.
• Partner with Clinical Enablement, Operations, Product, Customer Experience, and Logistics to support onboarding, utilization, retention, and reorder consistency.
• Maintain confidentiality, integrity, and availability of Protected Health Information in accordance with HIPAA standards.
• Demonstrate consistent quota attainment, competitive displacement, multi-site account growth, and regional KOL development.

What you will bring:

• 7 or more years of medical device or healthcare sales experience.
• 4 or more years in sleep diagnostics, cardiopulmonary diagnostics, neurology-adjacent diagnostics, or a closely related clinical market.
• Proven record of meeting or exceeding sales targets in a field-based territory role.
• Demonstrated ability to sell into complex healthcare environments with multiple stakeholders and longer buying cycles.
• Strong clinical curiosity and the ability to explain diagnostic differentiation without overcomplicating the message.
• Excellent communication, negotiation, executive presence, and presentation skills.
• Ability to operate independently, prioritize the right accounts, manage competing demands, and build structure in an underdeveloped territory.
• Proficiency with CRM systems, with Zoho experience preferred, and Microsoft Office Suite.
• Willingness to travel extensively throughout the Northeast region.
• Bachelor’s degree required. Advanced business, clinical, or healthcare-related education preferred.
• High ownership, strong field discipline, coachability, intellectual honesty, and comfort operating in a competitive growth-stage environment.

Mission Summary:

As a Principal Engineer within the Platform Engineering group, you will serve as the primary architect for Motional’s global cloud and hybrid infrastructure. Your mission is to evolve our current R&D environment into a hardened, cost-efficient, and scalable commercial backbone. You will lead the technical strategy for our hybrid cloud migration and ensure our internal frameworks provide  the velocity required for rapid AV deployment.

What you’ll be doing:

• Architect and drive the execution of our hybrid cloud strategy. You will design the networking, security, and compute abstraction layers that allow seamless workload mobility between on-premises high-performance computing (HPC) and AWS.
• Serve as the primary architect for our Kubernetes-based developer framework. You will identify and implement core improvements to the framework to enhance scalability, cost tracking and access control, reduce configuration complexity, and increase developer velocity.
• Lead organization-wide initiatives to harden service reliability. You will define the standards for observability, failover strategies, and production-grade stability across all platform-layer services.
• Prototype and deploy GenAI-focused tooling and agents to automate Platform Engineering tasks. This includes leveraging LLMs for automated incident response, predictive infrastructure scaling, and intelligent log analysis.
• Act as the final technical authority for infrastructure decisions, ensuring all new platform services align with long-term scalability and security goals.

What we’re looking for:

• Bachelor's or Master's in Computer Science, Engineering, or a related field (or equivalent practical experience)
• 12+ years in Software or Infrastructure Engineering, with significant experience in a Principal or Architect capacity
• Deep, expert-level knowledge of Kubernetes internals and ecosystem (EKS, Operators (KubeRay, ArgoWorkflow, Airflow), Service Mesh, GitOps)
• Proven track record of designing and migrating complex workloads between on-prem data centers and AWS
• Experience with Infrastructure-as-Code (Terraform/CloudFormation, Terraform Cloud/Spacelift/Atlantis) and advanced observability tooling (OpenTelemetry, Prometheus, Grafana, Datadog)
• Expert in architecting AI-driven operations, leveraging LLMs and agentic workflows to automate incident response, predictive scaling, and intelligent system observability
• Demonstrated ability to influence technical roadmaps across an entire engineering organization and mentor senior technical staff.

Bonus Points (not required):

• Expert-level experience managing and architecting solutions using Equinix datacenter products and services (e.g., Equinix Fabric, Network Edge, Metal) to enable high-speed hybrid cloud connectivity (AWS Direct Connect)
• Prior experience in the Autonomous Vehicle, Robotics, or large-scale IoT/Fleet Management industries.
• Experience leading technical projects in a globally distributed team
• Proven expertise in CI/CD orchestration, build systems (Bazel), frameworks

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

About the Position 

As a Data Scientist on the platform prediction team, you'll translate our probability of success predictions into measurable portfolio-level outcomes. You'll architect core systems — order management, execution simulation, portfolio construction, risk monitoring, and performance attribution — that let us rigorously evaluate signals from our AI-driven predictions in public and private equities and our internal portfolio.

This role sits at the intersection of quantitative finance, healthcare data, and AI-driven drug development. If you're excited about applying portfolio construction and risk management fundamentals to one of the most consequential prediction problems in healthcare, this is the role.No other company — hedge fund or pharma — has a technical data science position translating drug development experience into durable AI-native portfolio strategies. The skills you develop here — portfolio construction over assets with radically asymmetric risk profiles, clinical trial analytics, AI/ML in production, and risk management across multi-year horizons — can directly impact the delivery of new and effective therapeutics to patients by best aligning impactful medicines with economic incentives.

Responsibilities

• Work with the team to implement and maintain core portfolio engine: order management system, execution simulation layer, portfolio construction service, and performance tracking
• Design risk frameworks that quantify exposure across a portfolio of drug development bets with radically different risk profiles, timelines, and failure modes
• Run rigorous backtesting experiments with strict temporal constraints to evaluate Formation strategies against baseline approaches and measure marginal signal from new evidence sources
• Coordinate across the organization to integrate internal Formation data sources (clinical trial data, genomic evidence, real-world data) and proprietary tooling into portfolio analytics pipelines
• Work with product and engineering teams to build dashboards and reporting that communicate portfolio performance, risk metrics, and strategy comparisons to both technical and executive stakeholders
• Collaborate with the broader data science team to ensure portfolio-level evaluation feeds back into model improvement and evidence prioritization

About You 

Required Qualifications

• MS or PhD in a quantitative field (statistics, finance, physics, computational science, engineering, or related)
• 1-3 years in a quantitative research, data science, or analytics role — finance, healthcare, academic research, or consulting all count; substantive internships qualify
• Strong Python programming skills with experience in data-intensive workflows (pandas, numpy, scipy)
• Solid grasp of core portfolio construction and risk concepts: position sizing, rebalancing, Sharpe ratio, drawdown, volatility, benchmark comparison
• Demonstrated ability to work with messy, real-world datasets — comfortable with data wrangling, deduplication, and quality assessment
• Clear communicator who can present quantitative results to both technical peers and business stakeholders

Preferred Qualifications

• Experience with backtesting frameworks or portfolio simulation (vectorbt, Backtrader, or custom implementations)
• Exposure to healthcare, pharma, or biotech data (clinical trials, claims data, -omics, real-world evidence)
• Familiarity with alternative data in a research or investment context
• Experience with probability-of-success modeling, drug development decision analysis, or health economics
• Comfort with LLMs or AI/ML pipelines in a production or research setting
• Familiarity with dashboard/visualization tools (Streamlit, Plotly, Dash) and pipeline orchestration (Dagster, Airflow)

Healthcare OR finance domain knowledge is valued; both are not required.

Total Compensation Range: $154,500 - $202,000

About the Position

We're looking for a Data Engineer to join the Scientific Data Intelligence (SDI) team at Formation Bio to help build and scale the data infrastructure that powers our drug development platform. In this role, your primary focus will be supporting the Data Science team — building the coherent, well-structured data models, feature engineering pipelines, and evidence layers that underpin their scientific work. You'll also be responsible for ingesting data from a wide variety of sources—APIs, files, databases, and licensed third-party datasets—and transforming it into clean, analytics-ready assets using modern data tooling.

This is a foundational engineering role for someone who takes pride in building things right: reliable pipelines, well-structured data models, and systems that others can trust and build on. You'll work closely with Data Scientists and other engineers across the SDI team to ensure that high-quality data is consistently available where and when it's needed — and structured in a way that directly accelerates scientific discovery.

The ideal candidate is deeply fluent in Snowflake and dbt, has strong opinions about data modeling best practices, and has experience handling large and complex datasets across diverse ingestion patterns. You thrive in environments where data quality and engineering rigor are treated as first-class concerns.

Responsibilities

• Partner closely with Data Scientists and other teams to build coherent data models, feature engineering pipelines, and structured evidence layers that directly support scientific analysis and machine learning workflows.
• Design and build scalable ingestion pipelines to onboard data from diverse sources including REST APIs, flat files, relational databases, and licensed third-party datasets.
• Develop and maintain robust data models in dbt, adhering to best practices around modularity, testing, documentation, and layered architecture (staging, intermediate, mart).
• Orchestrate pipelines using Dagster to ensure reliable, observable, and maintainable workflows.
• Implement data quality checks, validation frameworks, and monitoring to ensure trustworthiness of datasets across the platform.
• Collaborate with Data Scientists and analysts to understand data needs and translate them into well-structured, reusable models.
• Handle large-scale data movement and transfer scenarios, applying appropriate tooling and patterns to ensure efficiency and reliability at scale.
• Document data models, pipeline logic, and transformation assumptions to support discoverability and data governance.

About You

• You have 5+ years of experience in data engineering, with a strong track record of building and maintaining production-grade pipelines.
• You are deeply fluent in dbt and follow best practices rigorously—including modular model design, sources and refs, testing (schema + data), documentation, and environment management.
• You have hands-on expertise with Snowflake, including schema design, performance tuning, and data governance patterns.
• You have experience with at least one modern orchestration tool—we use Dagster, but experience with Airflow or Prefect is equally welcome.
• You have broad ingestion experience across source types: REST APIs, flat files (CSV, JSON, Parquet), relational databases, and vendor-licensed datasets.
• You have worked with large datasets (TB -> PB scale) and understand the engineering considerations that come with scale—partitioning, incremental loading, efficient data movement, and storage optimization.
• You're a strong data modeler who thinks carefully about how data is structured, named, and layered for downstream usability.
• You value documentation, testability, and building things others can maintain and extend.
• You can balance upfront design with speed to execution, slowing down when it counts without getting stuck in the details.
• Bonus: You have experience with data quality and observability tooling such as Elementary, Great Expectations, or similar frameworks — and you treat data quality as a first-class engineering concern, not an afterthought.
• Bonus: You have experience with Spark, Databricks for large-scale data processing workloads.
• Bonus: You have experience with large-scale data transfer tooling such as AWS DataSync, AWS S3 Transfer Acceleration, or equivalent cloud-native data movement services.
• Bonus: You have experience in healthcare or life sciences data environments, including familiarity with EHR, claims, or other biomedical datasets.

Total Compensation Range: $177,500 - $232,000

Data is at the heart of every decision at Klaviyo. Our Analytics Engineering team sits within a hub-and-spoke model, partnering closely with Go-To-Market (GTM), Product, Engineering, and Business Intelligence teams to build scalable, trusted data systems. You’ll work at the intersection of business and engineering, owning core data models, enabling self-service analytics, and ensuring our data ecosystem is reliable, scalable, and compliant.

About the Role

We’re looking for a Senior Analytics Engineer to own end-to-end data pipelines and modeling across GTM (Sales) and Product domains. You’ll build and maintain the source of truth for operational and product data, enabling leadership, analysts, and product teams to answer complex questions with confidence. This role combines deep technical execution with high-impact business partnership, requiring you to translate ambiguous needs into scalable data products. You will operate as a trusted, embedded partner to stakeholders while leveraging the broader Data Engineering organization’s standards, tooling, and best practices. You’ll help build the foundation that powers everything from forecasting and compensation to product insights and experimentation. Please note that this is a hybrid role that requires 3 days/week in our Boston office. Fully remote candidates will not be considered at this time. 

How You’ll Make a Difference

• Own and deliver end-to-end data pipelines and models that power Sales and Product analytics
• Build curated, governed data marts that enable fast, reliable decision-making
• Design dimensional data models (dbt) for core entities such as accounts, pipeline, performance, and product usage
• Partner with Sales, Product, and Analytics teams to create holistic views of the customer and product lifecycle
• Raise the bar on data quality, testing, monitoring, and documentation
• Enable reverse ETL workflows to operationalize insights into business systems
• Act as a data ambassador across Product, Engineering, BI, and Legal, ensuring alignment on data contracts and governance
• Lead and mentor others through design reviews, best practices, and technical guidance
• Evolve internal tooling and infrastructure to improve developer and analyst productivity

What You’ll Do

• Data Modeling & Pipelines: Build and maintain production-grade dbt models in Snowflake
• Integrations & Ingestion: Own ingestion from systems like Salesforce, ERP, and product data sources
• Reverse ETL: Push high-value data models into downstream tools and workflows
• Data Quality & Governance: Implement testing frameworks, monitoring, and documentation to ensure trust and compliance
• Cross-Functional Partnership: Work closely with Sales Ops, Product Managers, Engineering, Finance, and GTM teams
• Repository Stewardship: Maintain dbt codebase, enforce best practices, and ensure scalability
• Tooling & Infrastructure: Contribute to Airflow, Terraform, AWS, and other platform tools as needed

Who You Are

• 5+ years of experience in analytics or data engineering in a modern data stack (Snowflake, BigQuery, or Redshift)
• Strong expertise in SQL and dbt, with a focus on building scalable, production-grade data models
• Proficiency in Python (or similar) for orchestration or tooling
• Experience owning end-to-end data workflows, from ingestion to business impact
• Ability to translate ambiguous business needs into structured data solutions
• Strong communication skills with the ability to partner with non-technical stakeholders
• Experience implementing data testing, monitoring, and governance frameworks
• Proven ability to lead cross-functional projects with minimal guidance

Nice to Have

• Experience with Sales Operations, compensation, or GTM analytics
• Familiarity with reverse ETL tools and workflows
• Experience with Airflow, Fivetran, Workato, or similar tools
• AWS experience (S3, EC2, Lambda) and infrastructure-as-code (Terraform)
• Exposure to experimentation frameworks or product analytics systems
• Experience with data privacy controls (masking, role-based access, compliance)

At Klaviyo, we value the unique backgrounds, experiences and perspectives each Klaviyo (we call ourselves Klaviyos) brings to our workplace each and every day. We believe everyone deserves a fair shot at success and appreciate the experiences each person brings beyond the traditional job requirements. If you’re a close but not exact match with the description, we hope you’ll still consider applying. Want to learn more about life at Klaviyo? Visit careers.klaviyo.com to see how we empower creators to own their own destiny.

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

What We Do

• The Omnichannel team at The Trade Desk is pioneering a new approach to digital advertising — one built around telling a coherent story through ads, using the media best suited for each chapter. We work across the full ad tech stack, from identity to measurement to bidding, to deliver unified campaign experiences that span screens, formats, and moments. Our Software Engineers are end-to-end owners who participate in designing, building, and delivering data-focused products for our stakeholders.

What You'll Do

• Design and build systems that orchestrate advertising across channels, ensuring a unified narrative from first impression to conversion.

• Work across team boundaries — partnering with identity, measurement, and bidding teams to integrate their capabilities into cohesive omnichannel solutions.

• Own projects end to end, from early design through production rollout and ongoing operation.

• Translate ambiguous business requirements into concrete technical designs and execution plans.

• Deliver high-quality changes with automated tests, metrics, and monitoring.

• Collaborate with product managers and business stakeholders to define what success looks like and drive toward it.

• Mentor other engineers and contribute to a culture of trust, ownership, and technical excellence.

Who You Are

• You're comfortable owning things end to end — from scoping a problem to shipping and operating the solution.

• You communicate clearly with both technical and non-technical audiences, and you're effective working across teams with competing priorities.

• You thrive in ambiguity. Vague requirements don't slow you down — you translate them into actionable plans and iterate toward the right answer.

• You have 7+ years of software development experience.

• You have strong experience with distributed systems and backend development in a language such as C#, Java, or Python.

• You have a solid understanding of computer science and engineering fundamentals.

• You're comfortable working on an agile, distributed team spanning multiple time zones.

Nice to Have

• Experience with large-scale data processing (Spark, Airflow, Kafka, Flink).

• Familiarity with cloud infrastructure and orchestration tools (Kubernetes, Terraform).

• Experience in ad tech or with real-time bidding systems.

• Exposure to cross-channel measurement or identity resolution.

We are seeking a talented and innovative Synthetic Data Engineer. In this role, you will design and implement domain-specific synthetic data generation pipelines, ensuring high-quality data management for training loops. Your expertise will drive the success of data processing and model training within the organization.

Responsibilities:

• Design domain-specific synthetic data generation (SDG) pipelines via self-instruct and constitutional prompting.
• Implement automated quality scoring and de-duplication systems.
• Manage data pipelines that feed directly into SFT and DPO training loops.

Qualifications:

• Proven experience building large-scale data pipelines (Airflow, Spark, Ray).
• Deep knowledge of prompt engineering for data generation.
• Familiarity with dataset distillation and bias mitigation.

Who We Are

PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning and artificial intelligence. We have a track record of success in deploying AI algorithms for histopathology in translational research, pathology labs and clinical trials.  Rigorous science and careful analysis is critical to the success of everything we do. Our team, composed of diverse employees with a wide range of backgrounds and experiences, is passionate about solving challenging problems and making a huge impact on patient outcomes.

Where You Fit 

As the Associate Director, MLOps Lead, you will lead the team responsible for the backbone of our AI/ML Stack: the infrastructure that bridges ML research and massive-scale production. Your primary directive is to evolve our stack to meet the next scale of needs in large scale ML training & inference workloads.  

You’re someone who enjoys designing and building for reliability, relishes collaboration and technical challenges, and takes pride in making things better – without taking yourself too seriously. Our technical space is broad: high-scale AI training & inference workloads, cloud infrastructure, Kubernetes, observability, distributed systems, and a bit of everything in between.

What You’ll Do

This role is critical for driving the scalability and efficiency of our Machine Learning Operations platform with high-impact & high growth strategic initiatives. 

• Vision and Roadmap: Develop and execute the long term vision & roadmap for MLOPs team to support ML development and deployment needs across the business units. Successfully manage the tension between short-term tactical deliveries and long-term architectural transformation for future growth. 
• Team Management: Lead and mentor a team of 6-7+ high-performing engineers. Strategically allocate resources to manage support for existing services while executing key strategic initiatives.
• Cross-Functional Collaboration: Partner with leaders across machine learning, data science, product engineering, and infrastructure to proactively identify pain points, address bottlenecks, and facilitate the deployment of new solutions.
• Foundation Model Readiness: Architect the compute and storage pipelines required for ML Engineers to manage millions of slides and complex derived artifacts without data fragmentation or synchronization latency.
• Inference Modernization: Modernize the AI Product inference stack to support 5-10x growth of AI runs across global deployments.
• System Observability: Collaborate with Site Reliability Engineering (SRE) to establish comprehensive metrics covering compute under-utilization, network bottlenecks, and granular cost and turn-around-time attribution.
• Technology Refresh: Conduct "Build vs. Buy" assessments, leading "Stack Refresh" audits to benchmark our proprietary tools against best-in-class commercial and open-source alternatives to meet our future needs.

What You Bring

To be successful in this role with us, you'll at least need:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience).
• 2-3+ years of experience managing engineering team(s), with a focus on building production-grade frameworks for MLOps or ML Infrastructure.
• Deep technical expertise with ML workloads on kubernetes, cloud computing platforms (AWS/GCP/Azure), workflow orchestration (Airflow, Kubeflow, or proprietary equivalents) and DevOps principles and infrastructure-as-code (Helm, Terraform).
• Proven experience managing petabyte-scale datasets and high-throughput production inference pipelines.
• Strong software engineering skills in complex, multi-language systems and experience with scalable service architecture.
• Use of AI assistants (e.g. CoPilot, Cursor, Claude) across platform development lifecycle.

It Would Be Great If You Also Have

• Exposure to ML frameworks like PyTorch or Scikit-learn.
• Experience with large-scale data processing frameworks (e.g. Spark, Hive, Databricks, Amazon EMR)
• Expertise in MLOps principles, including model lifecycle management, feature stores, model monitoring, and CI/CD for ML.
• Familiarity with security and compliance best practices in ML systems.

We Want To Hear From You

At PathAI, we are looking for individuals who are team players, are willing to do the work no matter how big or small it may be, and who are passionate about everything they do. If this sounds like you, even if you may not match the job description to a tee, we encourage you to apply. You could be exactly what we're looking for. 

PathAI is an equal opportunity employer, dedicated to creating a workplace that is free of harassment and discrimination. We base our employment decisions on business needs, job requirements, and qualifications — that's all. We do not discriminate based on race, gender, religion, health, personal beliefs, age, family or parental status, or any other status. We don't tolerate any kind of discrimination or bias, and we are looking for teammates who feel the same way.

The cash compensation outlined below includes base salary or hourly wage and on-target commission for employees in eligible roles. The summary below indicates if an employee in this position is eligible for annual bonus, overtime pay and equity awards. Individual compensation packages are tailored based on skills, experience, qualifications, and other job-related factors. 

Annual Pay Range:
AD, MLOps: $181,500 - $278,300

Not Overtime Eligible

Eligible for Equity

About Klaviyo

Klaviyo is a leading marketing automation platform that helps businesses grow by delivering personalized experiences. Our mission is to empower creators to own their destiny. We believe in providing an exceptional developer experience that enables our engineering teams to build and ship high-quality software efficiently and with confidence.

About the Team

The Messaging Infra organization at Klaviyo is focused on high-growth customer engagement through various messaging channels (email, SMS/MMS/RCS, mobile push, WhatsApp, and more). We work with multiple stakeholders to provide best-in-class service and reliability to connect a wide range of businesses, from entrepreneurs to enterprises, with their end users. The messaging platform is a mission-critical system, responsible for delivering billions of messages, and must be architected for massive scale and reliability.

As a Senior Lead Software Engineer within the Messaging Infra organization, you will serve as the primary technical architect for our high-volume message delivery systems while driving the overarching technical vision for channel expansion. You will partner with leaders across all channels to ensure a scalable, reliable, and performant platform strategy, acting as a key technical liaison to the broader Mobile Channels and Messaging Infrastructure pillar. Your leadership will ensure that our core systems are not just maintained, but architected to accelerate innovation and business impact while raising the bar on scalability and reliability.

What You'll Do

As a Senior Lead Software Engineer, you will have an overarching responsibility for the architecture, health, and scaling of the Messaging Infra platform with a focus on high-volume message delivery. Your role will involve:

• Technical Strategy: Define the technical strategy and architectural vision for the Messaging Infra platform, ensuring all system improvements align with long-term business goals for massive scalability, high reliability, and low latency message delivery.
• System Architecture and Improvement: Serve as the primary architect to build and improve high-volume message delivery systems across multiple channels, focusing on throughput, performance, and reliability.
• Cohesive Technical Vision: Drive technical leadership to guide architectural decisions across multiple teams, striving towards a cohesive technical vision across multiple roadmap items and providing solutions for complex problems around scalability and performance.
• Platform Rollouts and Product Execution: Oversee and execute the planning and seamless rollout of major software releases and updates for the platform, managing the entire lifecycle from design to deployment in production environments including significant individual contributions.
• Problem Solving and Reliability: Act as a go-to expert for diagnosing and resolving complex performance, scalability, and reliability issues across the messaging platform. Implement proactive monitoring and telemetry to identify and address problems before they become critical incidents.
• Mentorship: Mentor other engineers regularly, primarily experienced lead and senior engineers, to develop new technical leadership for the company.
• Cross-functional Collaboration: Partner with product managers, deliverability and compliance specialists, and other stakeholders to gather requirements, resolve conflicts, and ensure alignment across all teams and projects.
• Continuous Improvement: Stay current with emerging technologies and industry trends, evaluating new tools and practices to identify how they can improve systems and internal processes.

Who You Are

• 12 years of experience in software development, strong knowledge of computer science fundamentals, domain-driven design, and extensive expertise in architectural patterns, specifically within the domain of distributed systems.
• Have proven that you can build and scale complex distributed systems, including solving problems in sustainable ways, balancing pragmatic development with ideal architecture, breaking through technical obstacles with vigor, and leading teams through crises by working side by side with them in the trenches.
• BA or BS Degree in Computer Science, related field, or equivalent experience.
• Technical Expertise: Deep expertise in relevant programming languages (e.g., Python, Go) and cloud platforms (AWS, Azure, GCP), containerization (Kubernetes, Docker), and Infrastructure as Code (Terraform). You are particularly passionate and an expert in multiple stack areas, with strong and opinionated experience with various technologies, and know how to pick the right tool for any job.
• Technical Strategy: Demonstrated experience in contributing to the product and platform vision, as well as defining the strategy within the broader scope of long-term organizational needs.
• Architectural Skills: Strong understanding of software architecture, design patterns, and system integration, with the ability to define technical strategies and influence architectural decisions across multiple teams.
• Soft Skills: Exceptional problem-solving, analytical, leadership, and communication skills, with the ability to articulate complex technical concepts to both technical and non-technical audiences.
• AI Enthusiast: You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficiently.

Nice to Have

• Experience with the email and/or SMS ecosystem, including understanding of channel-specific protocols and security specifications (e.g., DKIM, DMARC, SPF), and familiarity with key 3rd-party entities (like mailbox providers, anti-abuse groups, and telecoms) and compliance rules/best practices.
• Background in performance optimization and a passion for digging into performance and scalability issues to drive breakthrough solutions.
• Familiarity with complex data management for testing, including large datasets and production-like data replication.

Our Tech Stack

• Python, Django, FastAPI, Celery
• Apache Kafka, Apache Pulsar, RabbitMQ, Spark, Airflow, and other tech from the big data stack
• MySQL, PostgreSQL, Clickhouse, Cassandra, Redis
• Typescript, React, Backbone.js, HTML, JavaScript, Babel, Jest, Webpack
• Graphite, statsd, Chronosphere
• AWS (EC2, RDS, Aurora, etc.), Terraform, Docker, Kubernetes (EKS), Pantsbuild, Argo, and other modern DevOps tools

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

Job Summary: The Senior Data Engineer is responsible for software development and data engineering projects. Identifies and executes tasks in the software development life cycle, reviews and debugs codes. They are responsible for ensuring the quality and functionality of software solutions. Collaborate with internal teams and provide technical guidance to deliver successful products. 

Duties/Responsibilities:

• Write well-designed, testable, efficient code
• Produce specifications and determine operational feasibility
• Integrate software components into a fully-functional software system
• Develop software verification plans and quality assurance procedures
• Document and maintain software functionality and APIs
• Configure and deploy software tools, processes and metrics
• Adhere to industry standards and regulations
• Embrace and evolve our agile development practices

Required Skills/Abilities: 

• Expert at developing complex software and data pipelines in Python, Airflow, DBT, Databricks, C# / .NET
• Expert with unit, integration and data quality testing
• Expert understanding of relational databases and open data formats and query concepts
• Excellent problem solving, coding and debugging skills
• Excellent verbal and written communication skills
• Time management skills with ability to meet deadlines
• Experience with GitHub and version control concepts, preferably Git
• Working experience with software/data engineer and design concepts
• Familiarity with distributed systems on AWS 
• Experience with Continuous Integration and Continuous Development
• Skilled GitHub and git
• Proven understanding of REST APIs and knowledge of how to build and call them

Education and Experience:

• BS degree in Computer Science, Engineering, Mathematics or relative experience
• 3+ years of experience software engineering
• Experience in healthcare or insurance industry preferred

Supervisory Responsibilities:

• None

Physical and or Travel Requirements:

• Prolonged periods of sitting at a desk and working on a computer 
• Occasional travel for company wide meetings

Salary Range:

• $120 – $160k USD

Summary

Klaviyo is looking for a Lead Data Science Analyst to join our GTM Strategic Analytics & Insights team. In this role, you will serve as a senior individual contributor at the intersection of advanced data science, AI/LLM-driven innovation, and Go-to-Market strategy. You will build and maintain sophisticated predictive and inferential models, conduct deep-dive statistical analyses, and develop AI-first solutions that unlock meaningful insights across the pre and post Sales Customer lifecycle.

The successful candidate will partner closely with GTM leadership to shape how Klaviyo understands, measures, and accelerates new business and customer outcomes; from pre-sales motion through onboarding, expansion and retention. You will operate with a strong bias toward AI-augmented workflows and bring a modern, LLM-aware approach to every analytical challenge.

The ideal candidate is intellectually curious, strategically minded, and energized by hard problems. They bring deep technical fluency across the full data science stack, a demonstrated ability to influence senior stakeholders through clear storytelling, and a genuine commitment to building AI-first solutions in a fast-moving SaaS environment.

How You Will Make a Difference

• Build and maintain advanced models: Build and maintain advanced predictive and time-series models: design, train, deploy, and monitor models across use cases such as demand forecasting, capacity planning, deal scoring, and customer propensity; incorporate seasonality, exogenous drivers, and backtesting frameworks to ensure accuracy and robustness
• Apply statistical rigor: lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and other statistical methods to surface actionable signals from complex, large-scale datasets
• Develop AI/LLM-powered solutions: architect and implement AI-first analyses and tooling using large language models, prompt engineering, retrieval-augmented generation (RAG), and related techniques to automate insight generation, surface qualitative signals at scale, and augment team capabilities
• Own forecasting and decision systems: Own end-to-end forecasting and operational decision systems, including time-series demand forecasting, capacity planning models (e.g., Erlang-based staffing), and production pipelines that power GTM and Support planning workflows; ensure reliability, scalability, and business adoption of outputs
• Drive customer intelligence: develop and maintain prospect, deal health, archetype, and capacity models that inform GTM strategy, planning, and growth initiatives
• Define the measurement framework: identify, create, and steward benchmarks and metrics that meaningfully represent growth, engagement, and success outcomes
• Communicate with impact: distill complex analyses into clear, cohesive narratives with executive-ready materials that drive decisions at the senior leadership level
• Collaborate cross-functionally: partner with Systems & Engineering, GTM Operations, Rev Ops & Planning, Product, Business Intelligence, Data Science, and Finance to ensure analytical solutions are integrated, scalable, and trusted

Who You Are

• 6+ years of professional experience in an advanced analytics or data science role; SaaS experience strongly preferred
• Deep expertise in statistical inference and modeling, including supervised techniques (regression, classification, gradient boosting, decision trees) and unsupervised techniques (clustering, PCA, anomaly detection, topic modeling)
• Hands-on experience designing and deploying AI/LLM-based solutions, including prompt engineering, fine-tuning, RAG pipelines, or LLM-integrated analytics workflows; you approach new problems with an AI-first mindset
• Familiarity and experience with distributed coding projects, including using Git for code management.
• Advanced proficiency in Python (pandas, numpy, scikit-learn, xgboost, statsmodels, and LLM/AI libraries such as LangChain, OpenAI SDK, or HuggingFace) and SQL; working knowledge of DBT
• Own and scale end-to-end data pipelines, including orchestration with Airflow and transformation/modeling with dbt; design reliable, testable, and modular workflows that support production-grade analytics and machine learning use cases, with a focus on performance, data quality, and maintainability.
• Develop and iterate on time-series forecasting frameworks using approaches such as ARIMA/SARIMAX, ETS, MSTL, and machine learning-based models; evaluate performance through rigorous backtesting and continuously improve model accuracy and business applicability
• Experience building data visualizations and dashboards across platforms such as Tableau, ThoughtSpot, matplotlib, seaborn, plotly, or similar tooling.
• Strong project ownership: experienced operating to a roadmap, managing milestones and deliverables, and delivering high-quality work product in a timely manner
• Comfortable with autonomy and ambiguity, with a proactive orientation toward identifying and solving problems before they're fully defined
• Excellent written and verbal communication skills, including experience preparing materials for executive audiences

Summary

Klaviyo is looking for a Lead Data Science Analyst to join our GTM Strategic Analytics & Insights team. In this role, you will serve as a senior individual contributor at the intersection of advanced data science, AI/LLM-driven innovation, and Go-to-Market strategy. You will build and maintain sophisticated predictive and inferential models, conduct deep-dive statistical analyses, and develop AI-first solutions that unlock meaningful insights across the pre and post Sales Customer lifecycle.

The successful candidate will partner closely with GTM leadership to shape how Klaviyo understands, measures, and accelerates new business and customer outcomes; from pre-sales motion through onboarding, expansion and retention. You will operate with a strong bias toward AI-augmented workflows and bring a modern, LLM-aware approach to every analytical challenge.

The ideal candidate is intellectually curious, strategically minded, and energized by hard problems. They bring deep technical fluency across the full data science stack, a demonstrated ability to influence senior stakeholders through clear storytelling, and a genuine commitment to building AI-first solutions in a fast-moving SaaS environment.

How You Will Make a Difference

• Build and maintain advanced models: Build and maintain advanced predictive and time-series models: design, train, deploy, and monitor models across use cases such as demand forecasting, capacity planning, deal scoring, and customer propensity; incorporate seasonality, exogenous drivers, and backtesting frameworks to ensure accuracy and robustness
• Apply statistical rigor: lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and other statistical methods to surface actionable signals from complex, large-scale datasets
• Develop AI/LLM-powered solutions: architect and implement AI-first analyses and tooling using large language models, prompt engineering, retrieval-augmented generation (RAG), and related techniques to automate insight generation, surface qualitative signals at scale, and augment team capabilities
• Own forecasting and decision systems: Own end-to-end forecasting and operational decision systems, including time-series demand forecasting, capacity planning models (e.g., Erlang-based staffing), and production pipelines that power GTM and Support planning workflows; ensure reliability, scalability, and business adoption of outputs
• Drive customer intelligence: develop and maintain prospect, deal health, archetype, and capacity models that inform GTM strategy, planning, and growth initiatives
• Define the measurement framework: identify, create, and steward benchmarks and metrics that meaningfully represent growth, engagement, and success outcomes
• Communicate with impact: distill complex analyses into clear, cohesive narratives with executive-ready materials that drive decisions at the senior leadership level
• Collaborate cross-functionally: partner with Systems & Engineering, GTM Operations, Rev Ops & Planning, Product, Business Intelligence, Data Science, and Finance to ensure analytical solutions are integrated, scalable, and trusted

Who You Are

• 6+ years of professional experience in an advanced analytics or data science role; SaaS experience strongly preferred
• Deep expertise in statistical inference and modeling, including supervised techniques (regression, classification, gradient boosting, decision trees) and unsupervised techniques (clustering, PCA, anomaly detection, topic modeling)
• Hands-on experience designing and deploying AI/LLM-based solutions, including prompt engineering, fine-tuning, RAG pipelines, or LLM-integrated analytics workflows; you approach new problems with an AI-first mindset
• Familiarity and experience with distributed coding projects, including using Git for code management.
• Advanced proficiency in Python (pandas, numpy, scikit-learn, xgboost, statsmodels, and LLM/AI libraries such as LangChain, OpenAI SDK, or HuggingFace) and SQL; working knowledge of DBT
• Own and scale end-to-end data pipelines, including orchestration with Airflow and transformation/modeling with dbt; design reliable, testable, and modular workflows that support production-grade analytics and machine learning use cases, with a focus on performance, data quality, and maintainability.
• Develop and iterate on time-series forecasting frameworks using approaches such as ARIMA/SARIMAX, ETS, MSTL, and machine learning-based models; evaluate performance through rigorous backtesting and continuously improve model accuracy and business applicability
• Experience building data visualizations and dashboards across platforms such as Tableau, ThoughtSpot, matplotlib, seaborn, plotly, or similar tooling.
• Strong project ownership: experienced operating to a roadmap, managing milestones and deliverables, and delivering high-quality work product in a timely manner
• Comfortable with autonomy and ambiguity, with a proactive orientation toward identifying and solving problems before they're fully defined
• Excellent written and verbal communication skills, including experience preparing materials for executive audiences

Are you passionate about data? How about leveraging data to drive meaningful impact across a fast growing two-sided marketplace? Have opinions on how to best enable data scientists across a billion-dollar company to do elastic workforce planning or real-time customer lifetime value prediction? Then we should definitely talk!

The Data Technology team at ezCater is growing! As we look towards the second half of 2025 and beyond, data is a key strategic component across the company - from advanced, real-time machine learning to business intelligence and data governance. Data is our differentiator and how we will drive real, meaningful impact to the $100+billion Catering industry.

We are hiring a Senior Data Engineer to join our expanding team in solving complex data and platform challenges to accelerate our growing business. The ideal candidate lives and breathes data while driving systems and architecture best practices. They care about driving business impact through producing solid and efficient infrastructure alongside accurate and performant data.  You will have the opportunity to work directly with executive stakeholders as we embark on a massive-scale data modeling effort across the organization, so flexibility and the ability to translate business requests to implementation are key.

What you’ll do:

• Write and ship a lot of code.
• Work directly with analysts and stakeholders to refine requirements, nail down logic, and debug and qualify produced data sets to ensure they meet the underlying business needs.
• Drive architectural decisions and thoughtfully balance trade-offs in system design.
• Lead cross-functional data initiatives.
• Champion high standards in data quality, security, and discoverability.
• Translate complex technical challenges into clear solutions.
• Design and develop high-performance data pipelines. Adhering to SDLC, including CI/CD and best practices.
• Identify and drive opportunities to optimize and/or scale existing parts of our stack.
• Utilize tooling and automation to improve developer efficiency.
• Monitor data systems to ensure quality and availability while seeking to drive down costs.
• Contribute to the team processes and community.
• Mentor other Data Engineers.
• Be part of our innovation and transformation story.
  
  
  

What you have:

• Strong experience with data warehousing, data lakes, ELT process, and enterprise data platforms such as Snowflake (preferred), Redshift & BigQuery.
• Experience with building performant data pipelines across disparate systems.
• Experience with cloud platforms such as AWS (preferred), GCP & Azure.
• Mastery in SQL and experience in Python.
• Ability to work independently and collaboratively.
• Willingness and ability to be part of an on-call rotation, occasionally working non-business hours to address critical system alerts and maintain service uptime.
• An open mind and willingness to be flexible. We have a large and complex business, and believe in driving real value out of every project we do.
• Our stack is Snowflake, dbt, Fivetran, Airflow, AWS, Sagemaker, MLFlow, Kubernetes + Docker, Monte Carlo, Hightouch and Python for custom ETL and data science integrations. Experience with the above is a nice-to-have, and a desire to learn is a must.
• Experience with marketing data is preferred, but not required. 
• Ability to travel up to 5 days per quarter for Together Weeks, team gatherings and other events, when applicable.
• A sharp mind, a soft heart and a large funny bone.
  

The national total target cash compensation range for this position, including base salary and bonus target, is $158,000–$190,000 annually.*

*Please note: Final offer amounts are determined by multiple factors, including prior experience, expertise and region & may vary from the amount above. This range does not represent additional compensation benefits (such as equity, 401K or medical, dental or vision insurance).

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.

About Toast

At Toast, we're building the restaurant platform that helps restaurants adapt, take control, and thrive. The Customer Success (CS) organization plays a pivotal role in helping customers get the most out of our products and we're transforming our data capabilities to drive a new era of proactive, data-informed customer engagement. 

The CS Data & Analytics team is at the center of this transformation, building the data infrastructure that makes proactive, data-informed customer engagement possible at scale for ~150,000 restaurant locations.

Role Overview

You'll be a founding member of a newly chartered data engineering function within Customer Success with a direct hand in shaping the architecture, tooling, domain model, and team culture from day one. This is a rare opportunity to build something from greenfield, with visibility to VP and senior CS leadership.

As a Data Engineer on the Customer Success Data & Analytics team, you’ll design, build, and scale the data platforms and pipelines that power insights across our Customer Success organization. Reporting to the Director of Data Infrastructure & Engineering, you’ll work closely with Data Infrastructure, Analytics, Engineering, and Product teams to ensure high-quality, reliable data is accessible and actionable.

This is a hands-on engineering role focused on delivering production-grade, scalable, and reliable data systems that support analytics, reporting, and operational decision-making across Customer Success.

A day in the life (Responsibilities) 

• Design, build, and maintain scalable, production-ready data pipelines (batch and real-time) for Customer Success using modern data technologies.
• Develop and evolve data models and schemas that enable reporting, analytics, and machine learning across Customer Success functions.
• Partner with CS operations leaders across care, workforce, and CX to translate business requirements into pipeline and infrastructure priorities, ensuring data work is directly connected to customer retention and operational outcomes.
• Ensure data quality, observability, lineage, and governance are built into every data workflow.
• Implement best practices for cost-efficient, performant cloud-based data infrastructure.
• Contribute to data platform tooling and frameworks that support self-service analytics and reusability across teams.
• Actively monitor and optimize data workflows for reliability, scale, and maintainability.
• Collaborate with cross-functional stakeholders to define and track key metrics and ensure consistent data definitions.
• Participate in code reviews, design discussions, and technical architecture planning to continually raise engineering standards.
• Build and maintain semantic and metrics layer foundations (e.g., dbt metrics, Snowflake semantic layer) that enable consistent, reusable definitions of CS KPIs across reporting and analytics surfaces.
• Contribute to the design and evolution of the CS data domain model including Customer 360, omnichannel interaction data, case management, and agent performance in close partnership with cross-functional stakeholders.
  
  

Governance & Standards

Partner with Toast's Business, Technology & Transformation (BTT) organization to establish data governance practices, security policies, and architecture standards that will serve as the foundation for CS data infrastructure. As a founding team member, you'll help define the norms, not just follow them.

• Ensure data pipelines, storage, and access patterns adhere to BTT data standards, security policies, and compliance requirements.
• Partner with BTT governance forums to align on design patterns, data architecture decisions, and SLA expectations.
• Maintain documentation and metadata for all data assets in accordance with established governance processes.
  
  

What you'll need to thrive (Requirements)

• 5+ years of experience in data engineering, software engineering, or a related role building and maintaining data systems in production.
• Strong SQL skills and experience with a programming language such as Python, Scala, or Java.
• Hands-on experience with cloud-based data platforms (AWS, GCP, or Azure).
• Experience building and optimizing ETL/ELT workflows using tools such as Airflow, dbt, or similar orchestration frameworks.
• Deep understanding of data modeling, data warehousing, and pipeline design.
• Ability to write clean, maintainable, and well-tested code for data transformations and automation.
• Demonstrated ability to communicate technical tradeoffs and data concepts clearly to non-technical stakeholders, including operations and business leaders.
• Proven ability to work collaboratively with analytics, operations, and engineering teams.
• Strong problem-solving skills, attention to detail, and a drive to build scalable, reliable systems.
  
  

What will help you stand out (Non-essential Skills/Nice to Haves)

• Experience building and optimizing data systems that operate at significant scale, managing billions of records across multiple data domains and systems, with the foresight to design for 5-year growth and future platform scale.
• Comfort using AI-assisted development tools (e.g., GitHub Copilot, Claude, Snowflake Cortex AI) to accelerate pipeline development, testing, and documentation and curiosity about how GenAI can enhance data product quality.
• Experience with real-time data streaming (Kafka, Kinesis, or Pub/Sub).
• Familiarity with modern data warehouse/lakehouse technologies (e.g., Snowflake, BigQuery, Redshift, Delta Lake).
• Experience with CI/CD, infrastructure as code, or containerization (e.g., Docker, Terraform).
• Exposure to data governance, metadata management, and observability tools.
• Background in SaaS or Customer Success analytics (usage data, retention metrics, customer health).
• Experience working with CS or CX-adjacent source systems such as Salesforce, Five9, Intercom, or similar CCaaS/CRM platforms and familiarity with the data models and integration patterns they produce.
• Knowledge of data security, privacy, and compliance in cloud environments.

AI at Toast

At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.

Our Total Rewards Philosophy

We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits.

Who We Are

As science and technology increasingly shape our lives, the Museum of Science strives to equip and inspire everyone to use science for the global good while keeping up with the pace of change. Among the world’s largest science centers and New England’s most attended cultural institution, we engage nearly five million people a year – at Science Park and in museums around the world, in classrooms, and online.

The Museum’s singular location connecting Boston and Cambridge puts us at the junction of some of the world’s most influential academic institutions and industries, local and state government, schools, and the public. Trusted by each sector, we are ideally positioned to convene, inspire, and create meaningful experiences for all. Come join us on our mission to inspire a lifelong love of science in everyone and living our vision for a world where science belongs to each of us for the good of all of us.

Why We Need You

Within the Museum of Science, the Facilities team plays a critical role in maintaining and advancing the infrastructure that supports one of the region’s most visited cultural institutions. Our team manages complex building systems, campus infrastructure, and capital improvements across a highly dynamic environment that welcomes millions of guests each year. From critical mechanical and electrical systems to large-scale renovation and sustainability initiatives, Facilities ensures the Museum remains safe, operational, and ready to deliver inspiring science experiences every day.

The HVAC Mechanic supports the safe and reliable operation of the Museum of Science’s complex mechanical systems. This role ensures the continuous performance of HVAC and building infrastructure systems that maintain comfortable, safe, and energy-efficient environments for visitors, staff, and exhibits.

What You’ll Accomplish

• Maintain Critical Mechanical Systems: Perform preventative, diagnostic, and corrective maintenance on HVAC and building mechanical systems to ensure safe, efficient, and reliable facility operations.
• Respond to Building System Needs and Operational Requests: Monitor and respond to alarms and operational needs through the Building Management System (BMS), adjusting environmental conditions such as temperature, airflow, and lighting to support museum operations.
• Support Rapid Response to Mechanical and Facility Issues: Prioritize and respond to service requests and mechanical issues to minimize downtime and maintain a safe and comfortable environment for staff and visitors.
• Collaborate Across Trades to Maintain Facility Operation: Work with licensed tradespeople and facilities staff to support a wide range of maintenance activities including electrical, plumbing, carpentry, painting, and general facility repairs.
• Maintain Accurate Maintenance Documentation: Assist in maintaining maintenance logs, equipment histories, and repair records to ensure clear communication and effective tracking of building systems and infrastructure.
• Support Emergency and Critical Facility Response: Respond to emergency incidents including fire alarms, system failures, building evacuations, and other emergency events as outlined in the Museum’s Emergency Preparedness and Management Plan.
• Maintain Safe and Organized Workspaces: Ensure maintenance shops and facilities areas remain clean, organized, and stocked so that facilities operations can proceed safely and efficiently.

What We’re Looking For (Competencies)

• Mechanical Troubleshooting: Ability to diagnose mechanical issues and perform effective repairs on HVAC and related building systems.
• Systems Awareness: Understanding of building mechanical systems, controls, and infrastructure, including Building Management Systems.
• Operational Reliability: Demonstrates accountability and responsiveness in maintaining critical building infrastructure that supports museum operations.
• Collaboration: Works effectively with facilities staff, licensed trades, and external contractors to maintain and repair facility systems.
• Communication: Clearly communicates maintenance needs, system conditions, and operational concerns to supervisors and colleagues.

Job-Specific Requirements

• Minimum 3 years of experience maintaining building mechanical systems or related mechanical trades experience
• Demonstrated mechanical aptitude and troubleshooting skills
• Strong organizational and communication skills
• EPA/CFC license preferred OR ability to obtain within first 12 months 
• Ability to respond to facility needs outside of scheduled hours when required

How We Work – Our Values

Mission-Focused: We inspire a lifelong love of science in everyone.

Audience-Driven: Everything we do begins with the people we serve.

Innovative: We are curious, take smart risks, share responsibility, and own outcomes.

Generous: We offer time and energy towards common goals.

About Klaviyo

Klaviyo is a leading marketing automation platform that helps businesses grow by delivering personalized experiences. Our mission is to empower creators to own their destiny. We believe in providing an exceptional developer experience that enables our engineering teams to build and ship high-quality software efficiently and with confidence.

About the Team

The Developer Infrastructure team at Klaviyo is at the heart of our engineering organization, dedicated to accelerating Klaviyo development. We build and maintain the tools and processes that allow engineers to maximize their focus on business objectives by minimizing time spent on build and deployment systems, foundational software "building blocks," and development environments. We are committed to fostering open communication and collaboration with our internal customers – the engineers.

As a Senior Lead Software Engineer within the Dev Infrastructure pillar, you will serve as the primary technical architect for our Dependencies and Platform domain while driving the overarching technical vision for the entire Dev Infra group. You will partner with leaders across CI/CD, Web Platform, Environments, Quality Tools & Testing and AI Enablement to ensure a cohesive infrastructure strategy, acting as a key technical liaison to the broader Core Infra organization. Your leadership will ensure that our core systems, dependency lifecycles, and platform upgrades are not just maintained, but architected to accelerate engineering velocity across the entire company.

What You'll Do

As a Senior Lead Software Engineer, you will have an overarching responsibility for the architecture, health, and dependencies for the Klaviyo code base with a focus on the backend. Your role will involve:

• Technical Strategy: Define technical strategy within the scope of the Klaviyo backend and execute to improve the area's architecture, products, infrastructure, and processes. 

• Strategic Planning and Architecture: Define the technical vision and architectural roadmap for the platform, ensuring all upgrades and dependency management align with long-term business goals for scalability, reliability, and security.
• Dependency Management: Lead efforts to determine, analyze, and optimize backend and third-party dependencies, ensuring they are secure, performant, and reliable. This includes establishing frameworks for risk assessment and contingency planning related to dependencies.
• Platform Upgrades and Rollouts: Oversee and assist with the planning and seamless rollout of major software releases and updates, managing the entire lifecycle from design to deployment in production environments including significant individual contributions.
• Enhance Developer Productivity: Drive initiatives to improve platform cycle time metrics across the board, including PR CI Speed, Human Code Review Pickup Time, and Commit Velocity by Repo. This involves a relentless focus on reducing toil and increasing engineering efficiency.
• Mentorship: Mentor other engineers regularly, primarily experienced lead and senior engineers. Ensure others in the area are developing skills they need to improve within the organization.
• Problem Solving and Reliability: Act as a go-to expert for diagnosing and resolving complex performance, scalability, and reliability issues across the platform. Implement proactive monitoring and telemetry to identify and address problems before they become critical incidents.
• Cross-functional Collaboration: Partner with product managers, security teams, hardware engineers, and other stakeholders to gather requirements, resolve conflicts, and ensure alignment across all teams and projects.
• Continuous Improvement: Stay current with emerging technologies and industry trends, evaluating new tools and practices to identify how they can improve systems and internal processes. 

Who You Are

• 12 years of experience in software development, strong knowledge of computer science fundamentals, domain-driven design, and extensive expertise in architectural patterns, specifically within the domain of distributed systems.
• BA or BS Degree in Computer Science, related field, or equivalent experience.
• Technical Expertise: Deep expertise in relevant programming languages (e.g., Go, Python) and cloud platforms (AWS, Azure, GCP), containerization (Kubernetes, Docker), and Infrastructure as Code (Terraform).
• Technical Strategy: Demonstrated experience in contributing to the product and platform vision, as well as defining the strategy within the broader scope of long-term organizational needs.
• Architectural Skills: Strong understanding of software architecture, design patterns, and system integration, with the ability to define technical strategies and influence architectural decisions across multiple teams.
• Soft Skills: Exceptional problem-solving, analytical, leadership, and communication skills, with the ability to articulate complex technical concepts to both technical and non-technical audiences.
• AI Enthusiast: You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient.

Nice to Have

• Experience with specific tools such as Pants, FastAPI, Django, React, Terraform, Buildkite.
• Background in performance optimization
• Familiarity with complex data management for testing, including large datasets and production-like data replication.

Our Tech Stack

• Python, Django, FastAPI
• Apache Kafka, Apache Pulsar, RabbitMQ, Spark, Airflow, and other tech from the big data stack
• MySQL, PostgreSQL, Clickhouse, Redis
• Typescript, React, Babel, Jest, Webpack
• Graphite, statsd, Chronosphere
• AWS, Terraform, Docker, Kubernetes, Pantsbuild, Argo, and other modern DevOps tools

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

About Klaviyo

Klaviyo is a leading marketing automation platform that helps businesses grow by delivering personalized experiences. Our mission is to empower creators to own their destiny. We believe in providing an exceptional developer experience that enables our engineering teams to build and ship high-quality software efficiently and with confidence.

About the Team

The Developer Infrastructure team at Klaviyo is at the heart of our engineering organization, dedicated to accelerating Klaviyo development. We build and maintain the tools and processes that allow engineers to maximize their focus on business objectives by minimizing time spent on build and deployment systems, foundational software "building blocks," and development environments. We are committed to fostering open communication and collaboration with our internal customers – the engineers.

As a Senior Lead Software Engineer within the Dev Infrastructure pillar, you will serve as the primary technical architect for our Dependencies and Platform domain while driving the overarching technical vision for the entire Dev Infra group. You will partner with leaders across CI/CD, Web Platform, Environments, Quality Tools & Testing and AI Enablement to ensure a cohesive infrastructure strategy, acting as a key technical liaison to the broader Core Infra organization. Your leadership will ensure that our core systems, dependency lifecycles, and platform upgrades are not just maintained, but architected to accelerate engineering velocity across the entire company.

What You'll Do

As a Senior Lead Software Engineer, you will have an overarching responsibility for the architecture, health, and dependencies for the Klaviyo code base with a focus on the backend. Your role will involve:

• Technical Strategy: Define technical strategy within the scope of the Klaviyo backend and execute to improve the area's architecture, products, infrastructure, and processes. 

• Strategic Planning and Architecture: Define the technical vision and architectural roadmap for the platform, ensuring all upgrades and dependency management align with long-term business goals for scalability, reliability, and security.
• Dependency Management: Lead efforts to determine, analyze, and optimize backend and third-party dependencies, ensuring they are secure, performant, and reliable. This includes establishing frameworks for risk assessment and contingency planning related to dependencies.
• Platform Upgrades and Rollouts: Oversee and assist with the planning and seamless rollout of major software releases and updates, managing the entire lifecycle from design to deployment in production environments including significant individual contributions.
• Enhance Developer Productivity: Drive initiatives to improve platform cycle time metrics across the board, including PR CI Speed, Human Code Review Pickup Time, and Commit Velocity by Repo. This involves a relentless focus on reducing toil and increasing engineering efficiency.
• Mentorship: Mentor other engineers regularly, primarily experienced lead and senior engineers. Ensure others in the area are developing skills they need to improve within the organization.
• Problem Solving and Reliability: Act as a go-to expert for diagnosing and resolving complex performance, scalability, and reliability issues across the platform. Implement proactive monitoring and telemetry to identify and address problems before they become critical incidents.
• Cross-functional Collaboration: Partner with product managers, security teams, hardware engineers, and other stakeholders to gather requirements, resolve conflicts, and ensure alignment across all teams and projects.
• Continuous Improvement: Stay current with emerging technologies and industry trends, evaluating new tools and practices to identify how they can improve systems and internal processes. 

Who You Are

• 12 years of experience in software development, strong knowledge of computer science fundamentals, domain-driven design, and extensive expertise in architectural patterns, specifically within the domain of distributed systems.
• BA or BS Degree in Computer Science, related field, or equivalent experience.
• Technical Expertise: Deep expertise in relevant programming languages (e.g., Go, Python) and cloud platforms (AWS, Azure, GCP), containerization (Kubernetes, Docker), and Infrastructure as Code (Terraform).
• Technical Strategy: Demonstrated experience in contributing to the product and platform vision, as well as defining the strategy within the broader scope of long-term organizational needs.
• Architectural Skills: Strong understanding of software architecture, design patterns, and system integration, with the ability to define technical strategies and influence architectural decisions across multiple teams.
• Soft Skills: Exceptional problem-solving, analytical, leadership, and communication skills, with the ability to articulate complex technical concepts to both technical and non-technical audiences.
• AI Enthusiast: You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient.

Nice to Have

• Experience with specific tools such as Pants, FastAPI, Django, React, Terraform, Buildkite.
• Background in performance optimization
• Familiarity with complex data management for testing, including large datasets and production-like data replication.

Our Tech Stack

• Python, Django, FastAPI
• Apache Kafka, Apache Pulsar, RabbitMQ, Spark, Airflow, and other tech from the big data stack
• MySQL, PostgreSQL, Clickhouse, Redis
• Typescript, React, Babel, Jest, Webpack
• Graphite, statsd, Chronosphere
• AWS, Terraform, Docker, Kubernetes, Pantsbuild, Argo, and other modern DevOps tools

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

Role Summary

Klaviyo is seeking a Manager of Advanced Analytics & Automation to lead strategic analytics programs and AI first automations for our Customer Success Strategy and Analytics team. This role owns the development of scalable customer intelligence systems, and spans customer cohorting, health and success signals, churn and expansion drivers, and AI-powered workflows that translate insight into action.

You will operate as a senior technical individual contributor with program-level ownership, combining advanced data analytics and applied AI in partnership to build durable tools and processes used across GTM. This role is ideal for someone who can architect, build, QA, and operationalize advanced analytics and insights projects end-to-end while partnering deeply across Product, Ops, and Data teams.

What You’ll Do

• Own Customer Success Intelligence & Advanced Analytics & Insights, design Production-Grade Data Models & Analytics Pipelines, and champion AI Tooling, Automation & Workflow Orchestration
• Build and maintain customer cohorting, segmentation, and benchmarking frameworks, in collaboration with our Product Data Science team, that captures meaningful differences in Customer behavior, maturity, and value
• Define and evolve customer health, adoption, and success scoring and signals, including leading indicators of churn and expansion
• Design and scale advanced analytics projects and models, including cohort analysis, statistical inference, and impact measurement systems, that connect product usage and engagement to customer retention outcomes
• Act as a technical program owner for complex analytics initiatives, driving roadmap definition, sequencing workstreams, coordinating dependencies, and delivering outcomes
• Create advanced SQL and Python outputs that power customer insights, forecasting inputs, and operational reporting, using GitHub for version control and change management, and applying strong data modeling and governance practices
• Partner closely with Data Engineering, CS OPs, Data Science and Product Analytics, to deliver strategic analytical tools and insights that inform GTM priorities, roadmap decisions, and customer strategy, and collaborate to deliver prototype AI-assisted tools that provide GTM teams real time actionable insights into customer behavior
• Design and deploy workflow automations using n8n, Airflow or other AI tools, and establish integrated AI driven alerts that surface real-time customer risk and opportunity
• Lead initiatives end-to-end: problem framing, design, build, QA, documentation, rollout, and iteration
• Maintain a high bar for execution quality, accuracy, reliability, and long-term scalability
• Translate complex technical outputs into clear, actionable narratives for senior business and GTM stakeholders

What We’re Looking For

• 5+ years of experience in advanced analytics, technical analytics or data analytics (SaaS strongly preferred)
• Advanced proficiency in SQL and GitHub, with experience owning complex analytical data models
• Strong hands-on experience with Python and/or R, including statistical inference, cohort analysis, and advanced research
• Tableau Developer–level fluency, with solid understanding of dashboard logic, metrics, and database structures
• Confident using AI tooling (e.g., Cursor, Glean, Codex, GitHub AI etc.) to accelerate development, and familiarity with workflow automation platforms (n8n, Airflow, DBT); experience with LangChain a plus
• Demonstrated ability to build and operationalize analytics and AI workflows used by CS, Sales, or GTM teams
• Experience operating as a technical program manager for analytics or data initiatives
• Strong communication skills and comfort partnering with senior, non-technical stakeholders
• High standards for analytical rigor, QA, documentation, and system reliability
• You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient. 

#LI-Abbey

#LI-hybrid

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

Role Summary

Klaviyo is seeking a Manager of Advanced Analytics & Automation to lead strategic analytics programs and AI first automations for our Customer Success Strategy and Analytics team. This role owns the development of scalable customer intelligence systems, and spans customer cohorting, health and success signals, churn and expansion drivers, and AI-powered workflows that translate insight into action.

You will operate as a senior technical individual contributor with program-level ownership, combining advanced data analytics and applied AI in partnership to build durable tools and processes used across GTM. This role is ideal for someone who can architect, build, QA, and operationalize advanced analytics and insights projects end-to-end while partnering deeply across Product, Ops, and Data teams.

What You’ll Do

• Own Customer Success Intelligence & Advanced Analytics & Insights, design Production-Grade Data Models & Analytics Pipelines, and champion AI Tooling, Automation & Workflow Orchestration
• Build and maintain customer cohorting, segmentation, and benchmarking frameworks, in collaboration with our Product Data Science team, that captures meaningful differences in Customer behavior, maturity, and value
• Define and evolve customer health, adoption, and success scoring and signals, including leading indicators of churn and expansion
• Design and scale advanced analytics projects and models, including cohort analysis, statistical inference, and impact measurement systems, that connect product usage and engagement to customer retention outcomes
• Act as a technical program owner for complex analytics initiatives, driving roadmap definition, sequencing workstreams, coordinating dependencies, and delivering outcomes
• Create advanced SQL and Python outputs that power customer insights, forecasting inputs, and operational reporting, using GitHub for version control and change management, and applying strong data modeling and governance practices
• Partner closely with Data Engineering, CS OPs, Data Science and Product Analytics, to deliver strategic analytical tools and insights that inform GTM priorities, roadmap decisions, and customer strategy, and collaborate to deliver prototype AI-assisted tools that provide GTM teams real time actionable insights into customer behavior
• Design and deploy workflow automations using n8n, Airflow or other AI tools, and establish integrated AI driven alerts that surface real-time customer risk and opportunity
• Lead initiatives end-to-end: problem framing, design, build, QA, documentation, rollout, and iteration
• Maintain a high bar for execution quality, accuracy, reliability, and long-term scalability
• Translate complex technical outputs into clear, actionable narratives for senior business and GTM stakeholders

What We’re Looking For

• 5+ years of experience in advanced analytics, technical analytics or data analytics (SaaS strongly preferred)
• Advanced proficiency in SQL and GitHub, with experience owning complex analytical data models
• Strong hands-on experience with Python and/or R, including statistical inference, cohort analysis, and advanced research
• Tableau Developer–level fluency, with solid understanding of dashboard logic, metrics, and database structures
• Confident using AI tooling (e.g., Cursor, Glean, Codex, GitHub AI etc.) to accelerate development, and familiarity with workflow automation platforms (n8n, Airflow, DBT); experience with LangChain a plus
• Demonstrated ability to build and operationalize analytics and AI workflows used by CS, Sales, or GTM teams
• Experience operating as a technical program manager for analytics or data initiatives
• Strong communication skills and comfort partnering with senior, non-technical stakeholders
• High standards for analytical rigor, QA, documentation, and system reliability
• You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient. 

#LI-Abbey

#LI-hybrid

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here