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.

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.

ABOUT THE JOB: 

As a Manufacturing Operations Leader, you will be instrumental in ensuring the consistent, safe, and efficient production of high-quality products. You will drive initiatives to enhance manufacturing capabilities, maintain operational excellence, and lead cross-functional teams to achieve production goals. This role requires a seasoned professional with a proven track record of commanding manufacturing environments and fostering a culture of continuous improvement.

WHAT YOU'LL DO:

• Ensure consistent, safe, and efficient production by leading initiatives to enhance product output and operational capabilities.
• Champion manufacturing optimization, driving cost reduction, quality improvement, productivity gains, and continuous improvement activities (including 5S) to boost utilization and uptime.
• Manage process capability and efficiency initiatives to reduce cycle times, increase yield, and prevent escapes.
• Champion cross-training and safety programs ensuring adherence to procedures, current training, incident documentation, and proactive hazard correction.
• Improve equipment, data and systems capability, manage Capex & asset lifecycle and integrate physical best practices
• Lead digital tool enhancements and integrate systematic best practices
• Support compliance with AS9100, FAR/DFAR, and MRA/MRL assessments, while collaborating on digital tool enhancements and integrating best practices.

QUALIFICATIONS:

• Bachelor’s degree in Engineering, Operations Management, Business Administration, or a related field.
• 8+ years of experience in manufacturing operations leadership, with a strong track record in driving production improvements and managing complex processes.
• Proven expertise in leading safety programs, implementing training, managing asset lifecycle and change control, and overseeing critical compliance standards (e.g., AS9100, FAR/DFAR).
• Demonstrated success in manufacturing optimization, including cost reduction, quality enhancement, productivity increases, and the application of continuous improvement methodologies (e.g., 5S).
• Strong communication and project management skills, with the ability to articulate strategic initiatives, manage cross-functional teams, and deliver concise leadership reports.
• Eligible to obtain and maintain an active U.S. security clearance.

PREFERRED QUALIFICATIONS:

• Experience with Lean and Six Sigma methodologies, including certifications or a proven track record of applying principles in projects.
• Experience integrating hardware, software, and firmware in end system products, encompassing functional and bring-up testing.
• Familiarity with Process Verification and Validation (V&V) practices, test cases, and overall strategy.
• Knowledge of manufacturing data systems and database structures (e.g., MySQL, Matlab, Airflow servers)

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

Shopmonkey's vision is to help every shop thrive by equipping them with the tools they need to run and grow their business. Our cloud based all-in-one shop management software takes owners and technicians from quote to cashing out a satisfied customer. Our software has a modern and intuitive UI and our backend is powered by the latest technologies so our clients can focus on the things they do best. As a Senior ML Engineer at Shopmonkey, you will be a part of a globally distributed engineering team working closely with your product and design counterparts. You will have the chance to work on the frontier of ML modeling, data pipelines, MLOps, and agentic AI systems to meet real-world auto shop needs. Shopmonkey has the structured data, workflows, and operational maturity to deliver ML powered systems that are not only intelligent but trusted and useful. You’ll move fast to bring ML systems from discovery all the way through production, helping to shape the future of the automotive care experience. For any Bay area based candidates, this would be hybrid with 2-3 days/week office at our Morgan Hill, CA office for collaboration. 

What you will do:

• Design, build, and ship production-ready ML models across a range of problem spaces: regression, classification, clustering, ranking, and recommendation systems.
• Conduct end-to-end development of ML systems: data gathering, experimentation, feature engineering, model training, evaluation, deployment, and monitoring.
• Define and track model performance metrics, run A/B tests, and iterate based on real-world feedback.
• Help design and implement shared feature stores so that reusable features can serve multiple models consistently in both batch and real-time contexts.
• Work within a modern MLOps environment to ensure scalable and reliable deployment of models.
• Contribute to training infrastructure, model versioning, and CI/CD pipelines for ML workflows.
• Work closely with data scientists and data engineers to develop data driven solutions that are high impact for businesses.
• Translate complex ML workflows into digestible updates for cross-functional stakeholders.
• Contribute to backlog velocity by owning appropriate tickets and delivering high-impact work in a collaborative, fast-paced environment.
• Implement NLP and LLM-powered components for sentiment analysis, real-time conversation evaluation, and behavior optimization.
• Contribute to analytics and predictive features such as no-show prediction and sentiment dashboards.
• Help build and ship AI agents that help automate key auto-shop business processes.

We are looking for people who have:

• Minimum of 5+ years of industry experience in applied machine learning; advanced degrees (Master’s or PhD) may offset years of experience.
• Proven experience shipping models into production (not just proof-of-concepts or notebooks).
• Proficiency in Python; experience with ML frameworks like PyTorch or Tensorflow.
• Strong foundations in classical ML/DL. Including some of the following: regression, classification, clustering, ranking, feature engineering, model evaluation, and experimentation.
• Bachelor’s degree in a STEM field, or equivalent practical experience.
• Strong collaboration and communication skills—comfortable working with PMs, designers, engineers and other cross functional team members.

Nice to have:

Candidates should have some experience in one or more of the following areas.

• Understanding of MLOps principles: model versioning, orchestration, evaluation, monitoring, model serving, and CI/CD for ML. 
• Understanding of MLOps, and experience with modern tooling like MLFlow, DVC, Airflow, etc.
• Experience with LLMs and NLP frameworks (e.g., TensorFlow, Hugging Face, LangChain).
• Experience with/interest in LLM workflows and agentic workflows
• Cloud infrastructure experience, (e.g. GCP, AWS).
• Familiarity with vector databases (e.g. Pinecone, pgvector) and embedding-based retrieval or similarity search.
• Strong SQL skills for working with large-scale data.
• Experience designing or contributing to feature stores (e.g. Feast, VertexAI Feature Store, Tecton) for shared, reusable feature pipelines.

Bonus Points:

• Prior experience working at a high growth startup.
• Experience building consumer-facing agents in vertical SaaS, in the automotive industry (business or consumers).
• Background in data processing or real-time analytics.
• Experience with Snowflake or other large-scale data warehouse solutions.

In the United States, the range is typically a salary of $165,000 to $200,000 + bonus + equity + benefits. The range provided is Shopmonkey’s reasonable estimate of the base compensation for this role. The actual amount will be based on job-related and non-discriminatory factors such as location, experience, training, skills, and abilities. Consult with your Recruiter during the initial call to determine a more targeted range based on these job-related factors. In addition to this base compensation company stock options and benefits as outlined below are included.

We're looking for a Software Engineer III to join the Contributory Network team within Data Acquisition - one of ZoomInfo's most strategically important initiatives.

The Contributory Network is building the infrastructure that powers a new generation of intelligence products by ingesting, transforming, and processing first-party data contributed by thousands of customers through their CRM, email, and recording provider integrations.

In this role, you'll build the data pipelines that ingest and transform massive volumes of contributed data, as well as contribute to front-end experiences that give internal teams and customers visibility into the platform. You'll work across the full stack, with a strong emphasis on backend data engineering and a meaningful front-end component.

What You'll Do

• Design and build data pipelines that ingest, validate, and transform first-party data from customer integrations (CRM systems, email providers, recording platforms)
• Develop and maintain ETL/ELT workflows for processing contributed contact information, opportunity metadata, and engagement signals at scale
• Build and enhance front-end interfaces for monitoring, configuration, and data quality dashboards using Angular
• Work with streaming and batch processing technologies to handle real-time and scheduled data flows
• Collaborate closely with product managers to translate business requirements into technical solutions
• Ensure data quality and integrity across the pipeline - from ingestion through enrichment and delivery
• Contribute to observability and monitoring for pipeline health, throughput, and error rates
• Partner with cross-functional teams (Data Quality, Platform, Product) to deliver impactful features

Must-Have Qualifications

Data Engineering

• 3+ years of professional software engineering experience
• Solid experience building and operating data pipelines (ETL/ELT) at scale
• Proficiency in Java
• Experience with data processing frameworks such as Apache Beam, Apache Airflow or Spark
• Hands-on experience with streaming technologies (Kafka, Pub/Sub, or similar)
• Familiarity with cloud platforms, preferably GCP (BigQuery, GKE, Dataflow/DataProc)
• Understanding of data modeling, schema design, and data quality practices

Front-End Development

• Working experience with modern front-end frameworks, ideally Angular
• Ability to build functional, clean UIs for internal tools, dashboards, and configuration screens
• Comfortable working with REST APIs and integrating front-end with backend services

General

• Bachelor's degree in Computer Science, Software Engineering, or a related field
• Strong problem-solving skills and attention to detail
• Ability to find the right balance between perfection and getting the job done
• Effective communicator who works well in a collaborative, cross-functional environment

Nice to Have

• Experience with Kubernetes (GKE/EKS) for distributed workloads
• Familiarity with Snowflake, BigQuery, or similar cloud data warehouses
• Experience with Terraform or infrastructure-as-code tools
• Exposure to data integration patterns involving CRM systems or email/calendar APIs
• Experience in a B2B data company or data-as-a-product environment

Why This Role Matters

The Contributory Network is a cornerstone of ZoomInfo's long-term strategy. By building the data infrastructure that processes first-party contributions from thousands of companies, you'll directly power intelligence products - from competitive benchmarking to buyer committee insights to market timing signals - that no other source in the world can offer.

You'll be joining at a pivotal moment: the initiative is high-visibility, the team is growing, and the opportunity to shape how contributory data flows through ZoomInfo is yours to build.

#LI-AR2

#LI-HYBRID

As a Principal Engineer on our Advertising, Company Intelligence, and Intent team, you’ll help design and implement the core systems that power our real-time marketing platform. From high-throughput ad infrastructure to large-scale identity resolution and intelligent systems enhanced by LLMs, push the boundaries of scale, intelligence, and speed—and deliver real-world business impact.

You’ll be joining a team with a broad range of ambitious, technically deep projects—from APIs that process tens of billions of events per day, to new buyer intent algorithms powered by LLMs, to a unified B2B identity graph. Depending on your strengths and interests, you’ll be matched to the initiatives where you can have the greatest impact.

This is a high-growth, high-ownership environment operating at the edge of what’s possible in modern software engineering, data infrastructure, and applied AI.

What You’ll Do

• Design and build distributed systems that process, enrich, and respond to billions of behavioral events per day in real time
• Develop high-performance APIs and services that support advertising, identity, and intent features across the Marketing Platform
• Leverage machine learning and large language models (LLMs) to analyze behavioral data, classify content, extract signals, and enable intelligent decision-making
• Build intelligent agents using frameworks like LangGraph or MCP to reason over data and power user-facing insights
• Design and operate data pipelines using tools like Kafka, Kinesis, and ClickHouse to support both streaming and batch workloads
• Drive quality, performance, scalability, and observability across all systems you own
• Collaborate cross-functionally with product managers, data scientists, and engineers to deliver customer-facing features and internal tooling
• Contribute to technical leadership and mentorship of teammates

What We’re Looking For

• 8+ years of backend, data, or infrastructure engineering experience, or equivalent impact and leadership.
  
  
• Strong experience in at least one of the following:
• Distributed systems engineering (e.g., building low latency high and throughput APIs, scalable microservices, event processing pipelines)
• Big data infrastructure (e.g., streaming, warehousing, low-latency storage at scale)
• Applied AI/ML, including use of LLMs for extraction, classification, or reasoning tasks
  
  
• Proficiency in one or more core languages: Java, Go, Python
• Solid grasp of SQL and large-scale data modeling
• Familiarity with databases and tools such as: ClickHouse, DynamoDB, Bigtable, Memcached, Kafka, Kinesis, Firehose, Airflow, Snowflake

AI-Native Development Mindset

• Comfortable using LLMs as part of your development workflow—whether via tools like Copilot, Cursor, ChatGPT, Claude, or others—to boost productivity, explore architecture ideas, and rapidly prototype.
  
  
• Skilled at designing and implementing LLM-powered systems such as RAG pipelines, agent frameworks (e.g., LangGraph), or intelligent workflows that reason across large datasets in real time.

Bonus Points

• Experience in ad tech, real-time bidding (RTB), or programmatic systems
• Background in identity resolution, attribution, or behavioral analytics at scale
• Contributions to open source in ML, infrastructure, or data tooling
• Strong product instincts and a passion for building tools that drive meaningful outcomes

#LI-SK1

#LI-Remote

We're looking for a Senior Software Engineer to join the Contributory Network team within Data Acquisition - one of ZoomInfo's most strategically important initiatives.

The Contributory Network is building the platform that ingests, transforms, and processes first-party data contributed by thousands of customers through their CRM, email, and recording provider integrations. This data powers a suite of intelligence products - from competitive benchmarking and buyer committee insights to predictive market timing - that are impossible to build any other way.

This role demands a driver. We need someone who takes ownership, pushes through ambiguity, unblocks themselves and others, and relentlessly moves work forward. You won't wait for perfect specs or complete clarity - you'll carve the path, make decisions, and deliver. If you thrive when given a hard problem and the autonomy to solve it, this is your role.

What You'll Do

• Own and drive the design and implementation of large-scale data pipelines that ingest, validate, transform, and enrich first-party contributed data from CRM systems, email providers, and recording platforms
• Architect resilient ETL/ELT pipelines handling massive volumes of contact data, opportunity metadata, engagement signals, and activity patterns
• Take initiative on complex technical challenges - identify problems proactively, propose solutions, and execute with urgency
• Build streaming and batch processing systems for real-time and scheduled data flows using Kafka, Pub/Sub, Apache Beam, or similar
• Establish data quality frameworks, ensuring accuracy, consistency, and completeness across contributed data
• Define and implement observability, monitoring, and alerting for pipeline health, throughput, cost, and data quality metrics
• Drive technical design decisions and guide implementations from concept to production
• Mentor and elevate other engineers on the team through code reviews, pairing, and knowledge sharing
• Partner with product, platform, and data science teams to deliver high-impact features on tight timelines
• Influence technical direction across the Contributory Network initiative and the broader Data Acquisition organization

Must-Have Qualifications

Data Engineering & Pipelines

• 5+ years of professional software engineering experience with a strong focus on data engineering
• Proven track record of building and operating production data pipelines at scale
• Deep experience with Python and/or Java
• Hands-on expertise with data processing technologies: Apache Beam, Apache Airflow, Spark, Google Dataflow, or DataProc
• Strong experience with streaming systems (Apache Kafka, Google Pub/Sub, or similar)
• Experience with cloud platforms, preferably GCP (BigQuery, GKE, Dataflow)
• Solid understanding of data modeling, schema evolution, and data quality management
• Experience designing and operating large-scale ETL/ELT pipelines processing terabytes of data

Technical Leadership & Drive

• Demonstrated ability to drive complex technical initiatives end to end - from scoping through delivery
• Track record of operating with high autonomy and a bias toward action
• Ability to push through ambiguity, make pragmatic decisions under uncertainty, and unblock progress
• Experience influencing technical direction within a team or across teams
• Strong code review and technical mentorship skills
• Proven ability to balance quality with velocity - you ship, iterate, and improve

General

• Bachelor's degree in Computer Science, Software Engineering, or a related field
• Exceptional interpersonal skills with a proven ability to build productive cross-departmental relationships
• Strong communicator who can explain complex systems to diverse audiences
• Entrepreneurial mindset - comfortable pioneering new capabilities and wearing multiple hats

Nice to Have

• Experience with Kubernetes (GKE/EKS) for running distributed workloads
• Familiarity with multi-cloud environments (GCP + AWS)
• Experience with Snowflake, BigQuery, Starburst/Trino, or similar query engines
• Experience with Terraform or infrastructure-as-code
• Knowledge of data integration patterns with CRM systems, email providers, or recording platforms
• Exposure to AI/LLM-based data processing approaches
• Experience in a B2B data company or data-as-a-product environment
• Experience with Apache Spark at expert level

What Sets This Role Apart

This isn't a role where you maintain existing systems and wait for tickets. The Contributory Network is a greenfield strategic initiative - you'll be building foundational infrastructure that directly powers intelligence products worth six figures from consulting firms, products that literally cannot exist without the data platform you build.

We need someone who goes through walls, not around them. Someone who sees a blocker and finds a way. Someone whose default mode is action, not analysis paralysis.

Why This Role Matters

The Contributory Network is ZoomInfo's strategic moat. It's how we pull away from competitors permanently by harnessing first-party data from thousands of companies to create intelligence no other source can offer. The data pipelines you build will power performance benchmarking, buyer committee intelligence, market timing signals, verified intent scoring, and fraud prevention - all at scale.

You'll be joining at a pivotal moment: the initiative is high-visibility, the team is growing, and the opportunity to define how contributory data acquisition is done at ZoomInfo is yours to shape.

#LI-AR2

#LI-Hybrid

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.