The Role

We are currently seeking a Staff Software Engineer, Infrastructure to join the AI Platform team that powers seamless insights and interaction through natural language and data intelligence across our AI products.

As a Staff Software Engineer, you’ll architect, build, and operate the backend and platform systems that power AI Platform. You’ll work across service design, distributed systems, cloud infrastructure, event-driven processing, observability, CI/CD, and production reliability, helping shape the technical direction of a platform that supports scalable, client-facing AI experiences.

This role requires a strong software engineering foundation combined with deep infrastructure and systems thinking. We are looking for an engineer who can write high-quality production code, make sound architectural tradeoffs, and own platform capabilities end-to-end — not someone focused only on scripting, cloud configuration, or infrastructure tooling in isolation.

You will collaborate closely with frontend, product, and AI/ML engineers to deliver reliable, secure, and scalable systems that align with Addepar’s standards of performance, resilience, and trust.

Applicants must have legal authorization to work in the country where this role is based on the first day of employment. Visa sponsorship is not available for this position.

What You’ll Do

• Design, build, and operate backend services and infrastructure that power AI Platform products.
• Write high-quality production code for platform and service layers, with strong emphasis on maintainability, reliability, and performance.
• Architect and evolve distributed systems that support AI Platform capabilities such as real-time interactions, asynchronous processing, and secure data workflows.
• Build and extend cloud-native infrastructure across core services, networking, storage, identity, and observability layers.
• Design and maintain infrastructure as code using Terraform, including reusable modules, environment management, and drift-safe workflows.
• Build and operate Kubernetes/EKS-based services, including deployment strategies, health checks, scaling configuration, and containerization workflows.
• Design and improve CI/CD pipelines, release workflows, and engineering productivity systems.
• Build and maintain asynchronous and event-driven systems using queues, RPC/service communication, and resilient failure-handling patterns.
• Design and evolve relational and NoSQL data models, including schema design, migrations, replication, and access patterns.
• Build and maintain observability across metrics, logs, tracing, dashboards, and alerting.
• Apply secure-by-default engineering practices across IAM, secrets, encryption, network isolation, and production operations.
• Mentor engineers through code review, design review, incident reviews, and technical guidance.
• Drive technical decisions that improve platform reliability, scalability, developer productivity, and long-term system health.

Who You Are

• Bachelor’s degree in Computer Science, Engineering, or related technical field (or equivalent practical experience).
• At least 8+ years of professional software engineering experience, including significant experience in backend, infrastructure OR platform oriented work.
• Strong proficiency in at least one production backend language such as Python, Go, OR Java and the ability to work effectively across a polyglot environment.
• Strong software engineering fundamentals, including system design, debugging, code quality, testing, and operational ownership.
• Deep hands-on experience with cloud infrastructure in production environments.
• Strong experience with infrastructure-as-code workflows such as Terraform or equivalent tools.
• Experience with Kubernetes/EKS, Docker, and modern deployment practices.
• Experience designing or extending CI/CD pipelines in production environments.
• Experience with distributed systems, microservice communication, message queues, asynchronous processing, and failure handling.
• Experience with both relational and NoSQL database design in production systems.
• Strong observability and production-debugging skills.
• Strong communication, collaboration, and mentoring skills.
• Ability to operate as a software engineer first: we are not looking for someone who is only strong in scripting, cloud configuration, or infrastructure tooling without corresponding depth in production software engineering.

Bonus

• Experience with AI/ML platform tooling such as Databricks, Spark, MLflow, or model-serving systems.
• Experience with real-time streaming or WebSocket-based systems at scale.
• Experience with multi-region architectures, data residency, or failover design.
• Experience in fintech or other highly regulated environments.
• Experience optimizing platform cost, performance, and reliability tradeoffs at scale.

About the role

We are seeking an accomplished Data Engineer to join our dynamic and rapidly expanding Tech Ops division. With significant projected growth, this is an opportunity to drive meaningful technical impact. In this role, you will work directly with customers, data vendors, and internal engineering teams to design, implement, and optimize complex data integration solutions within a modern, large‑scale cloud environment.

You will leverage advanced skills in distributed computing, data architecture, and cloud-native engineering to enable scalable, resilient, and high‑performance data ingestion and transformation pipelines. As a trusted technical advisor, you will guide customers in adopting Abacus’s core data management platform and ensure high-quality, compliant data operations across the lifecycle.

Your day to day

• Architect, design, and implement high-volume batch and real-time data pipelines using PySpark, SparkSQL, Databricks Workflows, and distributed processing frameworks.
• Build end‑to‑end ingestion frameworks integrating with Databricks, Snowflake, AWS services (S3, SQS, Lambda), and vendor data APIs, ensuring data quality, lineage, and schema evolution.
• Develop data modeling frameworks, including star/snowflake schemas and optimization techniques for analytical workloads on cloud data warehouses.
• Lead technical solution design for health plan clients, creating highly available, fault-tolerant architectures across multi-account AWS environments.
• Translate complex business requirements into detailed technical specifications, engineering artifacts, and reusable components.
• Implement security automation, including RBAC, encryption at rest/in transit, PHI handling, tokenization, auditing, and compliance with HIPAA and SOC 2 frameworks.
• Establish and enforce data engineering best practices, such as CI/CD for data pipelines, code versioning, automated testing, orchestration, logging, and observability patterns.
• Conduct performance profiling and optimize compute costs, cluster configurations, partitions, indexing, and caching strategies across Databricks and Snowflake environments.
• Produce high-quality technical documentation including runbooks, architecture diagrams, and operational standards.
• Mentor junior engineers through technical reviews, coaching, and training sessions for both internal teams and clients.

What you bring to the team

• Bachelor’s degree in Computer Science, Computer Engineering, or a closely related technical field.
• 7+ years of hands‑on experience as a Data Engineer working with large‑scale, distributed data processing systems in modern cloud environments.
• Working knowledge of U.S. healthcare data domains—including claims, eligibility, and provider datasets—and experience applying this knowledge to complex ingestion and transformation workflows.
• Strong ability to communicate complex technical concepts clearly across both technical and non‑technical stakeholders.
• Expert‑level proficiency in Python, SQL, and PySpark, including developing distributed data transformations and performance‑optimized queries.
• Demonstrated experience designing, building, and operating production‑grade ETL/ELT pipelines using Databricks, Airflow, or similar orchestration and workflow automation tools.
• Proven experience architecting or operating large‑scale data platforms using dbt, Kafka, Delta Lake, and event‑driven/streaming architectures, within a cloud‑native data services or platform engineering environment—requiring specialized knowledge of distributed systems, scalable data pipelines, and cloud‑scale data processing.
• Experience working with structured and semi‑structured data formats such as Parquet, ORC, JSON, and Avro, including schema evolution and optimization techniques.
• Strong working knowledge of AWS data ecosystem components—including S3, SQS, Lambda, Glue, IAM—or equivalent cloud technologies supporting high‑volume data engineering workloads.
• Proficiency with Terraform, infrastructure‑as‑code methodologies, and modern CI/CD pipelines (e.g., GitLab) supporting automated deployment and versioning of data systems.
• Deep expertise in SQL and compute optimization strategies, including Z‑Ordering, clustering, partitioning, pruning, and caching for large‑scale analytical and operational workloads.
• Hands‑on experience with major cloud data warehouse platforms such as Snowflake (preferred), BigQuery, or Redshift, including performance tuning and data modeling for analytical environments.

What we would like to see but not required:

• Experience in large-scale healthcare or payer data environments.

What you’ll get in return :

• Competitive Leave & Benefits
• Comprehensive health coverage
• Equity for every employee – share in our success
• Growth-focused environment – your development matters here

Work arrangements :

• Standard hours: 8 hours/day, 5 days/week
• Location: Pune, Hybrid (3 days a week in office)
• Shift: 12pm-9pm IST

About HG Insights

HG Insights is the pioneer of Revenue Growth Intelligence. For more than a decade, we have delivered comprehensive, AI-driven datasets on B2B buyers, technology adoption, IT spend, and buyer intent, sourced from billions of data points. Today, we are a trusted partner to Fortune 500 technology companies, hyperscalers, and innovative B2B vendors seeking precise go-to-market analytics and decision-making.

Through an evolving suite of AI agents that incorporate first-party data and buyer signals, HG Insights enables AI-powered GTM automation across sales, marketing, RevOps, and data analytics teams, modernizing GTM execution from strategy through activation.

Role Overview

The Applied Data Scientist - Research is a collaborative analytical partner to the Head of Data Science, contributing to the design and validation of GTM insights that power the Contextual Intelligence initiative.

You will co-develop insight logic, selecting signals, designing scoring frameworks, prototyping models in Python, and validating outputs. You will also contribute to the production-ready briefs that are implemented in the data production pipeline by the engineering team.

This role sits at the intersection of statistical modeling, structured data analysis, and applied AI. You are comfortable reasoning about how to measure something rigorously, how entities and relationships in a knowledge graph can be leveraged, and how to use LLMs as a practical tool in the insight development workflow, not as a subject of research, but as part of the toolkit.

What You Will Do

Insight & Model Development

• Co-develop scoring frameworks and metrics models, contributing to signal selection, weighting logic, and model structure across a range of GTM insight types (acquisition,expansion, retention, strategic)
• Prototype insight logic in Python notebooks: assembling features from HG's structured data assets, implementing model components, and stress-testing outputs.
• Design and run validation experiments to confirm that insight outputs are directionally correct, well-calibrated, and meaningful across the full vendor universe
• Contribute to ontology and entity design, thinking through how vendors, products, companies, and relationships should be structured to support a given insight, informed by a conceptual understanding of the knowledge graph schema

Production Brief Development

• Translate insight designs into clear, implementation-ready production briefs 
• Document model specifications precisely: component definitions, feature engineering, aggregation logic, edge case handling, and expected output distributions
• Participate in handoff reviews with the production function, answering implementation questions and refining specs based on feasibility feedback

Insight Research & Discovery

• Contribute to the prioritized insights catalog, researching new insight ideas, assessing data availability, and framing feasibility
• Stay current on GTM data science approaches, competitive intelligence methodologies, and relevant analytical techniques that could expand the insight library

What We're Looking For

Core Skills

• Statistical modeling depth: Ability to design and implement a range of scoring and metrics models from first principles; comfortable with component weighting, normalization, signed rate-of-change metrics, composite aggregation, and distribution analysis; knows when a technique is appropriate and why 
• Python for analytical prototyping: Strong notebook-based Python for data manipulation, feature construction, model prototyping, and output validation; pandas, NumPy, and Scikit are daily 
• SQL: Proficient in querying structured data at scale; used for signal extraction, feature derivation, and validation checks across large vendor and company datasets
• Analytical rigor & validation thinking: Ability to critically evaluate whether a model is measuring what it claims to measure; designs validation experiments, checks edge cases, and flags when outputs don't pass a sanity check
• Clear technical communication: Able to translate analytical logic into precise written specifications; the production brief is a key deliverable 

Applied AI & Graph Literacy

• LLM API usage: Hands-on experience using Claude, GPT, or equivalent APIs as a practical tool; can design effective prompts, integrate LLM steps into an analytical workflow, and evaluate output quality critically
• Knowledge graph concepts: Conceptual understanding of how entities, relationships, and properties are structured in a graph; able to reason about how graph-derived features (e.g., vendor-product-company traversals) should inform insight design, without necessarily writing production Cypher 

Nice to Have

• GTM/Management Consulting, or IT Research experience, familiarity with concepts like install base, intent signals, competitive intelligence, and market analysis. Experience writing Cypher or querying graph-structured data directly
• Experience working collaboratively with engineering, product and GTM teams
• Experience in a B2B SaaS or data products environment

Tools & Environment

Primary

• Python (pandas, NumPy, scipy, Jupyter)
• SQL
• LLM APIs (Claude, GPT)
• Git and version control

Working Knowledge

• Databricks
• Cloud storage 
• Knowledge graph concepts 

The Role

We are currently seeking an Associate Portfolio Data Analyst to join our Portfolio Data Feeds Team. Addepar provides asset owners and advisors a clearer financial picture at every level, allowing them to make more informed and timely investment decisions.

As an Associate Portfolio Data Analyst focusing on our portfolio data platform, you will be responsible for integrating client portfolio data into Addepar’s leading portfolio management products. You will work closely with data providers, internal partners and engineering to expand Addepar’s capabilities and coverage to meet client needs. You will partner closely with leaders across the business to build the preeminent data solutions and services that serve investment teams in wealth management and beyond. In addition, you work with other partners within the product management team on a variety of initiatives to improve overall data management and integration across Addepar.

Applicants must have legal authorization to work in the country where this role is based on the first day of employment. Visa sponsorship is not available for this position.

What You’ll Do

• Analyze and onboard portfolio data content from a variety of sources using industry standard analysis tools (such as SQL and Jupyter Notebooks) into determining how to integrate and model that data into Addepar’s data integration ecosystem
• Partner with engineering, product management, and data operations to deliver high quality, timely and reliable data solutions through Addepar’s data network
• Work with a variety of 3rd party data providers to develop and support the integration of their data into our platform
• Develop and support data aggregations and functionality for our clients based on client and 3rd party content and user workflow needs
• Develop data-oriented workflow solutions and product improvements in collaboration with user experience, design, and application focused project management teams
• Analyze and develop solutions to improve data operations and content pipeline efficiency
• Understand Addepar’s products and clients. Work with counterparts within the business to drive new and innovative opportunities to serve our clients and grow the business
• Contribute to the evolution of Addepar’s financial concordance solutions to better serve our clients and cement Addepar’s role as the outstanding integrator of market traded and alternative asset investments
• This is a role that will allow the successful candidate to build on their experience in the investment domain

Who You Are

• 1+ years of experience working with financial data and concepts that are relevant to Addepar’s clients and products, or graduated from a business related degree
• Must have relevant domain-specific experience in wealth management and portfolio management.(Stocks, bonds, securities, etc.)
• Technical skills preferred in any or all of the following: Excel, SQL, Python, pyspark, Databricks or other common financial services systems and applications
• Good communication and interpersonal skills working with vendors, clients and internal partners
• Passion for the finance and technology space and solving previously intractable problems at the heart of investment management.

About the role

We are seeking an accomplished Data Engineer to join our dynamic and rapidly expanding Tech Ops division. With significant projected growth, this is an opportunity to drive meaningful technical impact. In this role, you will work directly with customers, data vendors, and internal engineering teams to design, implement, and optimize complex data integration solutions within a modern, large‑scale cloud environment.

You will leverage advanced skills in distributed computing, data architecture, and cloud-native engineering to enable scalable, resilient, and high‑performance data ingestion and transformation pipelines. As a trusted technical advisor, you will guide customers in adopting Abacus’s core data management platform and ensure high-quality, compliant data operations across the lifecycle.

Your day to day :

• Architect, design, and implement high-volume batch and real-time data pipelines using PySpark, SparkSQL, Databricks Workflows, and distributed processing frameworks.
• Build end‑to‑end ingestion frameworks integrating with Databricks, Snowflake, AWS services (S3, SQS, Lambda), and vendor data APIs, ensuring data quality, lineage, and schema evolution.
• Develop data modeling frameworks, including star/snowflake schemas and optimization techniques for analytical workloads on cloud data warehouses.
• Lead technical solution design for health plan clients, creating highly available, fault-tolerant architectures across multi-account AWS environments.
• Translate complex business requirements into detailed technical specifications, engineering artifacts, and reusable components.
• Implement security automation, including RBAC, encryption at rest/in transit, PHI handling, tokenization, auditing, and compliance with HIPAA and SOC 2 frameworks.
• Establish and enforce data engineering best practices, such as CI/CD for data pipelines, code versioning, automated testing, orchestration, logging, and observability patterns.
• Conduct performance profiling and optimize compute costs, cluster configurations, partitions, indexing, and caching strategies across Databricks and Snowflake environments.
• Produce high-quality technical documentation including runbooks, architecture diagrams, and operational standards.
• Mentor junior engineers through technical reviews, coaching, and training sessions for both internal teams and clients.

What you bring to the team :

• Bachelor’s degree in Computer Science, Computer Engineering, or a closely related technical field.
• 5+ years of hands‑on experience as a Data Engineer working with large‑scale, distributed data processing systems in modern cloud environments.
• Working knowledge of U.S. healthcare data domains—including claims, eligibility, and provider datasets—and experience applying this knowledge to complex ingestion and transformation workflows.
• Strong ability to communicate complex technical concepts clearly across both technical and non‑technical stakeholders.
• Expert‑level proficiency in Python, SQL, and PySpark, including developing distributed data transformations and performance‑optimized queries.
• Demonstrated experience designing, building, and operating production‑grade ETL/ELT pipelines using Databricks, Airflow, or similar orchestration and workflow automation tools.
• Proven experience architecting or operating large‑scale data platforms using dbt, Kafka, Delta Lake, and event‑driven/streaming architectures, within a cloud‑native data services or platform engineering environment—requiring specialized knowledge of distributed systems, scalable data pipelines, and cloud‑scale data processing.
• Experience working with structured and semi‑structured data formats such as Parquet, ORC, JSON, and Avro, including schema evolution and optimization techniques.
• Strong working knowledge of AWS data ecosystem components—including S3, SQS, Lambda, Glue, IAM—or equivalent cloud technologies supporting high‑volume data engineering workloads.
• Proficiency with Terraform, infrastructure‑as‑code methodologies, and modern CI/CD pipelines (e.g., GitLab) supporting automated deployment and versioning of data systems.
• Deep expertise in SQL and compute optimization strategies, including Z‑Ordering, clustering, partitioning, pruning, and caching for large‑scale analytical and operational workloads.
• Hands‑on experience with major cloud data warehouse platforms such as Snowflake (preferred), BigQuery, or Redshift, including performance tuning and data modeling for analytical environments.

What we would like to see but not required:

• Experience in large-scale healthcare or payer data environments.

What you’ll get in return :

• Competitive Leave & Benefits
• Comprehensive health coverage
• Equity for every employee – share in our success
• Growth-focused environment – your development matters here

Work arrangements :

• Standard hours: 8 hours/day, 5 days/week
• Location: Pune, Hybrid (3 days a week in office)
• Shift: 12pm-9pm IST

About the team: 

The Customer Operations team is at the heart of Forma.ai's mission. This team has a direct impact on the growth of Forma.ai. They are results-driven and solutions-minded. 

The Customer Operations team works closely with our customers, helping them to understand and take advantage of all the features Forma.ai offers and ensuring that they get the most value from the platform.  

What you'll be doing:  

Reporting & Dashboarding

• Design, maintain, and enhance dashboards in BI tools (e.g., Looker Studio, Salesforce/HubSpot reports) to monitor marketing campaign performance, sales pipeline health, lead flow, and conversion metrics 
• Automate recurring reports and implement self-serve analytics capabilities for GTM teams 

Data Analysis & Insights 

• Analyze funnel performance from top-of-funnel marketing campaigns to bottom-of-funnel sales outcomes
• Provide regular insights into key KPIs like campaign ROI, customer acquisition cost (CAC), and attribution across channels
• Support A/B testing initiatives, sales activity analysis, and segmentation strategies 

Scripting & Automation 

• Write Python and SQL scripts to extract, clean, and structure data from external sources (e.g., job boards, press releases, M&A feeds, web scraping APIs)
• Build automated enrichment pipelines to augment CRM and marketing data with third-party insights (e.g., firmographics, hiring activity, technology stack, funding events) 

Data Quality & Tooling 

• Take ownership of data cleanliness and integrity across GTM systems (e.g., Salesforce, HubSpot, Databricks), including investigating issues, proposing solutions, and manually resolving historical data problems when necessary
• Maintain and improve key GTM logic — including lead scoring models, lifecycle stage transitions, attribution frameworks, and related automation rules — ensuring they are well-defined, consistent, and actionable 

What we're looking for:  

• Background in Engineering, Commerce, Mathematics and/or Statistics
• Natural curiosity about AI and emerging technologies — especially where they intersect with automation, data, and workflow orchestration across the GTM stack
• Familiarity with B2B go-to-market motions and how to measure their effectiveness
• Experience in report building, data analysis and workflow automation within a GTM tech stack (e.g. Salesforce, HubSpot, Gong, BI tools) 
• Proficiency in SQL or Python and familiarity with at least one BI tool (e.g., Power BI, Looker, Tableau)
• 3-5 years of related experience
• High achiever with a strong sense of ownership
• Ability to take ownership and run tasks in a fast-paced and evolving environment 

Strategic Project Lead / Delivery Manager 

Data Annotation & AI Delivery

8+ Years of Experience

Position Overview

We are looking for a seasoned Delivery Manager (8+ years of experience) to lead large-scale data annotation and curation projects in support of cutting-edge LLM and multimodal AI research. You will oversee end-to-end delivery for projects with 100+ team members, ensure world-class data quality, and drive process and tooling innovation in collaboration with AI researchers and cross-functional teams.

This role is ideal for someone who combines deep technical expertise, proven leadership in managing large teams, and a passion for advancing AI/ML innovation. Given that our key customers operate in the data analytics, data engineering, and Business Intelligence (BI) space, the ideal candidate will bring hands-on leadership experience with platforms such as Databricks and Snowflake, enabling them to deeply understand customer domains, speak their language, and deliver annotation outcomes that are contextually relevant and technically sound.

Primary Responsibilities

1. Team Leadership & Development

• Lead, mentor, and grow large-scale teams of annotators and trainers.
• Drive performance management, career development, and targeted training.
• Foster a culture of excellence, ownership, and continuous learning.
• Design and implement motivation strategies (including gamification) to boost engagement and reward top performers.

1. Project Management & Coordination

• Define and manage project scope, objectives, timelines, and quality metrics.
• Align with cross-functional stakeholders on goals and deliverables.
• Proactively identify and resolve delivery risks, blockers, and dependencies.
• Provide clear and consistent reporting on progress, challenges, and solutions.

1. Data Quality & Integrity

• Own quality control and data integrity across the annotation lifecycle.
• Analyze datasets to identify trends, anomalies, and insights for improvement.
• Implement best practices for annotation, evaluation, and data curation.
• Share learnings and continuously improve quality processes.

1. Technical Collaboration & Innovation

• Partner with engineering and research teams to optimize internal tooling.
• Stay ahead of emerging trends in AI, ML, and multimodal processing.
• Champion innovations that reduce task completion times and improve efficiency.
• Maintain clear and scalable documentation for processes and training.

1. Customer & Domain Engagement

• Serve as a domain-knowledgeable point of contact for customers operating in data engineering, analytics, and BI environments.
• Leverage hands-on experience with platforms like Databricks and Snowflake to understand customer data workflows, terminology, and quality expectations.
• Translate customer-specific data engineering and BI contexts into clear, actionable annotation guidelines and quality benchmarks for delivery teams.
• Collaborate with customers to define annotation taxonomies and evaluation criteria that align with their data pipeline and analytics use cases.

Required Skills & Qualifications

• 10+ years of experience in engineering, program management, or delivery of large-scale projects.
• Proven ability to lead and scale large teams (100+ members) with measurable impact.
• Strong technical grounding in AI/ML, data annotation, and multimodal systems.
• Hands-on leadership experience in data analytics, data engineering, or BI domains, with working knowledge of platforms such as Databricks and/or Snowflake (architecture, pipelines, data modeling, and production workflows).
• Familiarity with the modern data stack including ETL/ELT pipelines, data warehouses, data lakes, and BI tools (Power BI, Tableau, Looker, or similar).
• Ability to translate complex data engineering and analytics concepts into annotation frameworks, quality standards, and training materials.
• Proficiency in Python (preferred) or Swift for data analysis and automation.
• Strong mathematical and analytical skills for data-driven problem solving.
• Experience in data curation, annotation pipelines, and quality control.
• Excellent communication and stakeholder management skills.
• Track record of driving high-quality outputs under tight deadlines.

Preferred Skills & Qualifications

• Advanced degree in Computer Science, Data Science, AI, or related fields.
• Experience applying gamification to workforce engagement.
• Familiarity with modern project management tools & methodologies.
• Exposure to state-of-the-art multimodal AI technologies.
• Prior experience managing annotation or data labeling projects for customers in the data engineering, analytics, or BI space.
• Familiarity with SQL at an advanced level and data orchestration tools such as Apache Airflow, dbt, or Prefect.
• Certifications in Databricks (e.g., Data Engineer Associate) or Snowflake (e.g., SnowPro Core) are a plus.

About the Role

We are looking for a Data Modeler to join a global data modeling team responsible for transforming business requirements into conceptual and logical data models. This role requires understanding of healthcare data domains (Claims, Membership, Enrollment, Provider, Billing, Interoperability - FHIR, HL7, ADT, CCDA, etc.) and experience in enterprise data modeling best practices.

The Data Modeler will work closely with the Director of Data Modeling and Senior Data Modelers to adhere to data modeling standards, metadata governance, and follow data architecture strategies that align with business and technical needs. They will also collaborate with business analysts, product analysts, client implementation teams, and data governance teams to ensure models support operational and analytical use cases, high performance, and data integrity.

This is an exciting opportunity for a data modeler with experience in Databricks, Snowflake, schema evolution, and modern data engineering practices to shape the future of healthcare data platforms.

Your day to day

Identify and define requirements for canonical data models as well as consumption use cases required by enterprise healthcare customers. Your activities will include:

• Perform data profiling analysis.
• Use SQL, data modeling and data integrity principles.
• Build conceptual and logical data models aligned with enterprise architecture, business requirements and industry standards.
• Follow data modeling best practices, including schema evolution, lossless data modeling, metadata management, and data versioning.
• Use enterprise data modeling tools (Erwin, ER/Studio, DBT, or similar).
• Ensure models support analytical (data science, actuarial, reporting) and operational (Claims, Care Management) use cases.
• Maintain metadata, business glossary, entity relationship diagrams and data dictionaries to support data lineage and governance.
• Partner with Client Management, Product, and Business Analyst teams to understand client needs, assess data model impact, and ensure data models and related artifacts meet or exceed expectations.
• Work with client management teams to provide support and data model expertise during client implementation.

What you bring to the team

• 2+ years of experience in data modeling, enterprise data architecture, and cloud-based data engineering.
• Expertise in enterprise data modeling tools (Erwin, ER/Studio, DBSchema, or similar).
• NoSQL data model experience.
• Strong hands-on experience with cloud data platforms (Databricks, Snowflake) and data lakehouse architectures.
• Deep understanding of healthcare data models across Core Payer (Claims, Membership, Enrollment, Provider, Billing, Premium) and Clinical Interoperability (FHIR, HL7, ADT, CCDA, etc.) domains.
• Experience with schema evolution, lossless data modeling, and data versioning strategies.
• Strong background in SQL, data profiling, and metadata creation and maintenance.
• Ability to learn from senior modelers, enforce modeling best practices, and drive adoption of enterprise data standards.
• Excellent collaboration skills to work with data architects, engineers, and client implementation teams.

What we would like to see, but not required

• Healthcare reporting experience.
• Experience with OBT, data lakehouse, and metadata management frameworks.
• Familiarity with business intelligence (BI) and reporting solutions on modern cloud data warehouses.
• Knowledge of APIs, SDKs, and data distribution frameworks for enabling real-time and batch data access.
• Experience with data governance, security, and regulatory compliance in healthcare data management.

What you’ll get in return 

• Competitive Leave & Benefits
• Comprehensive health coverage
• Equity for every employee – share in our success
• Growth-focused environment – your development matters here

Work arrangements 

• Standard hours: 8 hours/day, 5 days/week
• Location: Pune, Hybrid (3 days a week in office)
• Shift: 12pm-9pm IST

About the Role

As a Product Owner in the AI Solutions team, you will play a pivotal role in translating strategic direction into actionable product increments. You will work closely with engineering teams in India, Nepal, and the US to ensure that our generative AI platform delivers measurable value to healthcare payers, with a focus on key business cases (i.e. Payment Integrity) and unstructured data solutions. This is an execution-focused role, responsible for the “how” of delivery and sprint cadence, while closely partnering with the Senior Product Manager who drives the “what” and “why.”

Your day to day

• Create and maintain a structured product backlog for GenAI solutions, ensuring clear problem statements, acceptance criteria, and definitions of done.
• Own GenAI stories in Jira, maintaining distinct boards, sprint cadences, and capacity visibility as defined by team processes.
• Break down high-level product and governance objectives into actionable user stories and tasks.
• Lead grooming sessions with engineers, data modeling, stakeholders, and others to clarify scope, estimate effort, and prioritize work based on business value and readiness.
• Drive sprint planning, coordinate daily stand-ups for the AI team, and proactively remove blockers to ensure commitments are met and deliverables align with the roadmap.
• Define and execute acceptance testing for completed features, verifying that data traceability, outputs, and governance capabilities meet requirements.
• Coordinate with security and compliance to ensure features meet auditability, observability, and hallucination control guardrails.
• Prepare release notes, user guides, and internal training materials; support deployments with DevOps and ensure client-facing documentation reflects new capabilities.
• Act as a liaison between product leadership, engineering, data modeling, and platform teams; communicate progress and issues clearly.
• Track and report on key SDLC metrics (burndown, velocity, throughput, capacity) and maintain a knowledge base of FAQs, lessons learned, and key decisions.
• Champion continuous improvement by applying agile best practices, identifying process bottlenecks, and leveraging AI tools for backlog management and documentation.

What you bring to the team

• 3+ years as a Product Owner or similar role in a data-driven environment, ideally supporting AI, data platforms, or healthcare products.
• Experience working with distributed engineering teams, preferably across US and India time zones, and familiarity with Scrum/Agile methodologies and tools (e.g., Jira, Confluence).
• Demonstrated ability to translate strategic product direction into actionable backlog items, groom stories with engineers, and manage sprint cadence.
• Strong understanding of the software development lifecycle (SDLC) and release management, with the ability to coordinate acceptance testing and quality assurance.
• Exposure to healthcare data (claims, clinical, or payer workflows) and AI/ML technologies.
• Excellent communication and organizational skills; comfortable interfacing with technical and non-technical stakeholders across product, engineering, security, and sales.
• Fluency in English (required for India-based roles).

What we would like to see, but not required

• Experience with healthcare interoperability standards (e.g., HL7 V2, FHIR, X12).
• Experience with cloud technologies (AWS, Azure, GCP), Databricks, or Snowflake.
• Prior work in a high-growth, fast-paced startup environment.

What you’ll get in return 

• Competitive Leave & Benefits
• Comprehensive health coverage
• Equity for every employee – share in our success
• Growth-focused environment – your development matters here

Work arrangements 

• Standard hours: 8 hours/day, 5 days/week
• Location: Pune, Hybrid (3 days a week in office)
• Shift: 12pm-9pm IST

The Role

As a Data Solutions Consultant, you will support some of Addepar’s complex and high-touch client data implementation projects. You’ll design scalable solutions to transform and integrate clients’ portfolio data into Addepar, and build expertise across both technical and business domains.

You’ll also be among the first members of the Data Solutions team based in India, helping establish our presence in the region while working closely with colleagues across the U.S., U.K., and other global offices. This is a collaborative role offering the opportunity to shape our operating model locally and strengthen how our global teams deliver together.

Beyond client delivery, you’ll also have the opportunity to participate in internal  innovation; contributing to  automation, tooling, and best practices that enable scale and elevate how we operate globally. This is an individual contributor position for someone who thrives at the intersection of finance, data, and technology, and is excited to make a global impact from India. 

Applicants must have legal authorization to work in the country where this role is based on the first day of employment. Visa sponsorship is not available for this position.

What You’ll Do

• Support  complex client implementations: execute data conversion projects to extract, transform, and load historical portfolio data from clients’ legacy systems into Addepar
• Build  scalable data solutions:  Translate client requirements into flexible, sustainable workflows that enable accurate reporting and analysis, with guidance from senior team members
• Contribute to  a portfolio of high-impact projects:  Manage multiple concurrent client engagements, prioritizing effectively and driving each to successful completion
• Collaborate across teams: Building knowledge of  investment-related data and partner closely with Services, Sales, Product, and Engineering to deliver tailored client outcomes
• Champion operational excellence:  Identify and lead opportunities to streamline, automate, and improve our delivery processes and workflows

Who You Are 

• Experienced: 2+ years of experience in technology, finance, or consulting, with a strong record of leading complex data or financial system integration projects
• Technical: Proficiency in Python and SQL. You should be comfortable navigating large datasets, streamlining data flows (ETL), and finding ways to automate repetitive manual tasks to improve efficiency. Experience/familiarity with Databricks and API workflows is a plus.
• Investment-Savvy: 
• A foundational grasp of the investment landscape. You understand how different assets work—like stocks, bonds, and derivatives—and you’re familiar with the basics of how portfolios are tracked and accounted for.
• Experience working directly with the building blocks of investment data. You’ve handled things like security identifiers, trade records, and market pricing updates, and you understand how this information flows through a financial firm.
• Technical Translation: Ability to articulate technical architectures to non-technical internal teams, converting technical requirements into clear, actionable business value.
• Solutions-Oriented: Curious problem-solver who takes initiative and is developing  ownership and accountability in ambiguous environments
• Collaborative: Eager to work across teams and functions to deliver measurable impact
• Process-Minded: Highly organized, detail-oriented, and continuously looking for ways to improve efficiency and scale
• Adaptable: Thrives in a fast-paced environment with evolving priorities and global stakeholders

A Note on Requirements

Don’t check every single box? Apply anyway. We are looking for curious, motivated individuals. If you are passionate about the intersection of finance and tech but don't meet every specific qualification listed above, we still want to hear from you and see if your unique skills could be a fit for our team.

Important Note: This role will require you to work from Pune office 3 days a week in UK shift i.e. 2:30 PM to 11:30 PM IST. (hybrid role)

Overview of Role

We are seeking a highly skilled Cloud Data Engineer to join our Managed Services team. In this role, you will be the technical backbone for our enterprise clients, responsible for designing, building, and maintaining high-performance data platforms using Snowflake and Azure Databricks. You will act as a trusted advisor, ensuring that our customers' data ecosystems are scalable, secure, and cost-optimized across multi-cloud environments.

Key Responsibilities

• Platform Engineering: Design and implement end-to-end data lakehouse architectures using Azure Databricks (Delta Lake),Azure HDInsights and Snowflake Cloud Data Warehouse.
• Pipeline Development: Build scalable, metadata-driven ETL/ELT pipelines using Azure Data Factory (ADF), PySpark, and SnowSQL, including ingestion workflows from HDInsight-managed Hadoop and Kafka clusters.
• Managed Services Support: Provide Tier-3 technical support for client environments, including performance tuning, complex bug fixes, and architectural optimizations.
• Multi-Cloud Integration: Oversee data movement and integration strategies across Azure, AWS, and GCP to ensure seamless "Data Everywhere" capabilities for clients.
• Cost & Performance Governance: Monitor and optimize Snowflake credit consumption and Databricks cluster usage to ensure clients receive maximum ROI.
• Security & Compliance: Implement enterprise-grade security including Role-Based Access Control (RBAC), Row-Level Security (RLS), and data masking (Dynamic Data Masking in Snowflake / Unity Catalog in Databricks).

Required Technical Skillset

Experience & Qualifications

• Experience: 5+ years in Data Engineering, with at least 3 years of hands-on experience in both Snowflake and Databricks.
• Managed Services Background: Proven experience supporting external customers with strict SLAs and complex governance requirements.
• Multi-Cloud (Plus): Direct experience deploying Snowflake or Databricks on AWS or GCP is highly desirable.
• Certifications (Preferred): * SnowPro Core or Advanced Architect
• Databricks Certified Data Engineer Professional
• Microsoft Certified: Azure Data Engineer Associate (DP-203)

About HG Insights

Headquartered in Santa Barbara, California, HG Insights is the global leader in technology intelligence. We help the world’s most innovative companies accelerate their go-to-market efforts with precision through advanced data science methodologies and proprietary data assets. We offer a culture that blends innovation, collaboration, and growth, where each team member is empowered to make a measurable impact.

Role Overview

We are looking for a Staff/Senior Machine Learning Engineer to join our growing AI and Data Platform team in Pune, India. In this role, you will be responsible for designing, building, and scaling ML systems that power our core data intelligence products. You’ll work at the intersection of data engineering and machine learning, collaborating closely with data scientists, software engineers, and product teams to turn models and agents into robust, production-ready systems.

This is a high-impact role suited for someone who thrives on ownership, scalability, and deploying real-world ML and AI solutions at scale.

Key Responsibilities

ML & AI System Development

• Design and build end-to-end ML pipelines, from data ingestion and feature engineering to model training, serving, and monitoring.
• Productionize AI agents, including LLM-backed workflows, multi-step tool use, and API orchestration.
• Collaborate with data scientists to bring experimental models into stable, high-performing production systems.
  
  

MLOps & Infrastructure

• Implement and maintain MLOps workflows (CI/CD for ML), tracking experiments, managing model versions, and automating retraining.
• Set up observability for models and agents, including tracing, logging, latency/error tracking, and fallback logic.
• Ensure infrastructure supports scalability, performance, and compliance requirements.
  
  

Technical Leadership & Collaboration

• Lead technical architecture for key AI and ML systems, ensuring maintainability and extensibility.
• Partner with product and platform teams to translate business needs into reliable and performant ML solutions.
• Mentor junior engineers and help elevate technical practices across the engineering org.

Minimum Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 8+ years of industry experience, with at least 4+ years in ML engineering or MLOps.
• Strong programming skills in Python and experience with ML libraries such as PyTorch, TensorFlow, and scikit-learn.
• Experience deploying AI agents in production, including retrieval-augmented generation (RAG), multi-step reasoning, and tool-use orchestration.
• Familiarity with LangChain, LlamaIndex, Haystack, or similar frameworks for agent development.
• Hands-on with LLMs (OpenAI, Anthropic, Cohere, etc.), embedding models, and vector store integration.
• Expertise in MLOps tools like MLflow, Airflow, SageMaker, or Kubeflow.
• Experience with cloud platforms (preferably AWS), Docker/Kubernetes, and large-scale data systems (e.g., Snowflake, Databricks).
• Excellent problem-solving and system design skills, including CI/CD, testing, and infrastructure-as-code.
  
  

Preferred Qualifications

• Experience with streaming pipelines, online inference, and model monitoring at scale.
• Exposure to agent memory, feedback loops, or AI workflow orchestration in customer-facing products.
• Prior contributions to open-source ML or LLM projects are a plus.
  
  

Why Join HG Insights?

• Work on high-impact AI products used by global enterprise customers.
• Lead the ML engineering strategy and shape our AI platform.
• Competitive compensation and benefits tailored for India-based employees.
• A collaborative and innovation-focused team environment.
  
  

Ready to build the future of AI infrastructure? Apply now to join HG Insights as a Staff Machine Learning Engineer in Pune.

About the Position

Reporting to the Software Engineering Manager and as a member of the Development team at IDC, the Associate Software Engineer supports the development of high quality and performant software solutions that address the diverse needs of our customers.

In this role, you will collaborate with senior engineers and cross-functional teams to develop and maintain software systems, contribute to data engineering initiatives, and support the full software development lifecycle. This position offers an opportunity to grow technical expertise while working on large-scale data systems and modern cloud technologies.

Candidate Requirements and Qualifications

• 1–3 years of experience in Python programming with a basic understanding of data structures and algorithms.
• Exposure to developing or maintaining data pipelines for structured or unstructured datasets.
• Familiarity with Python-based data processing and web scraping tools such as Beautiful Soup or similar libraries.
• Basic knowledge or exposure to cloud-based data technologies such as Azure Data Lake, Azure Data Factory, or Azure Databricks is desirable.
• Working knowledge of SQL or similar relational databases is required.
• Exposure to additional programming languages such as C#, Java, or similar is a plus.
• Strong problem-solving mindset and ability to write clean, maintainable code.
• Good communication and collaboration skills, with a willingness to learn from senior engineers and contribute to team success.
• Demonstrated curiosity and eagerness to learn new technologies in a fast-paced engineering environment.

Key Accountabilities & Duties

• Assist in developing, testing, and maintaining data pipelines and software systems that support large-scale data processing.
• Support efforts to improve data reliability, performance, and quality within existing systems.
• Work collaboratively with software engineers, data analysts, and data scientists to prepare and manage datasets used in analysis and applications.
• Participate in code reviews, testing, and debugging to ensure software quality and performance.
• Contribute to the development and maintenance of databases and data processing systems.
• Follow best practices in software development, including documentation, testing, and version control.
• Support the team in maintaining and improving data engineering workflows and processes.
• Continuously learn new tools, frameworks, and technologies to improve development capabilities.

FEQ425R230

As a Solutions Architect (Analytics, AI, Big Data, Public Cloud), you will guide the technical evaluation phase in a hands-on environment throughout the sales process. You will be a technical advisor internally to the sales team, and work with the product team as an advocate of your customers in the field. You will help our customers to achieve tangible data-driven outcomes through the use of our Databricks Lakehouse Platform, helping data teams complete projects and integrate our platform into their enterprise Ecosystem. You'll grow as a leader in your field, while finding solutions to our customers' biggest challenges in big data, analytics, data engineering and data science problems.

The impact you will have:

• You will be a Big Data Analytics expert on aspects of architecture and design
• Lead your clients through evaluating and adopting Databricks including hands-on Apache Spark™ programming and integration with the wider cloud ecosystem
• Support your customers by authoring reference architectures, how-tos, and demo applications
• Integrate Databricks with 3rd-party applications to support customer architectures
• Engage with the technical community by leading workshops, seminars and meet-ups
• Together with your Account Executive, you will form successful relationships with clients throughout your assigned territory to provide technical and business value

What we look for:

• Strong consulting / customer facing experience , working with external clients across a variety of industry markets
• Core strength in either data engineering or data science technologies
• 8+ years of experience demonstrating technical concepts, including demos, presenting and white-boarding
• 8+ years of experience designing architectures within a public cloud (AWS, Azure or GCP)
• 6+ years of experience with Big Data technologies, including Apache Spark™, AI, Data Science, Data Engineering, Hadoop, Cassandra, and others
• Coding experience in Python, R, Java, Apache Spark™ or Scala

About us:

HG Insights is the pioneer of Revenue Growth Intelligence. For more than a decade, we have delivered comprehensive, AI-driven datasets on B2B buyers, technology adoption, IT spend, and buyer intent, sourced from billions of data points. Today, we are a trusted partner to Fortune 500 technology companies, hyperscalers, and innovative B2B vendors seeking precise go-to-market analytics and decision-making.

Through an evolving suite of AI agents that incorporate first-party data and buyer signals, HG Insights enables AI-powered GTM automation across sales, marketing, RevOps, and data analytics teams, modernizing GTM execution from strategy through activation.

What You'll Do:

We’re searching for an experienced Full Stack developer to work on our product teams who is passionate about developing products that are simple, intuitive, and beautiful. We build big-data systems utilizing cutting-edge technologies and solutions that allow our developers to learn and develop while shipping amazing products.

As part of our team, you’ll be working on our Big Data Insights Platform products and elevating user experiences. Our stack is based on Elixir, React, and GraphQL APIs. We are a growing company with small, focused engineering teams that are delivering innovative features in a fast-growing market.

What You’ll Be Responsible For:

• You will work on our Product Engineering Teams
• You will design and enhance core business-driving services and products
• You will develop features in our databases, backend apps, and front end UI
•  You will help architect and design service-driven UI via RESTful and GraphQL APIs
• You will work on ideas from different team members as well as your own
• Participate in our on-call rotation, Fix bugs rapidly and investigate and resolve production problems
• Attend daily stand-up meetings, planning sessions, encourage others, and collaborate at 
  a rapid pace

What You’ll Need:

•  BS/MS in Computer Science, or other related fields. Or on-the-job experience.
• 5+ years of designing and programming in a work setting 
• Proficient in backend (Elixir) and frontend (JavaScript/TypeScript) with real-world experience applying system and code design patterns.
• Strong in React or similar
• Experience building RESTful or GraphQL APIs
• Good knowledge of SQL
• Experience with Amazon Web Services (EC2, S3, RDS, Lambdas, EKS, etc.)
• Comfortable working with CI/CD and automation tools: Docker, Kubernetes, Terraform or
  similar
• Good DevOps skills (automate everything, infrastructure as code)
• Comfortable in an agile development environment
• Self-learner, hacker, and technology advocate who can work on anything
• Thrive in a fast-growing environment
• Proven track record of successful project delivery
• Excellent written and spoken English communication

Nice-to-haves:

•  You've worked on Enterprise-grade SaaS applications
• Experience leading a project / team
• Familiar with ElasticSearch, Snowflake/Databricks, ClickHouse or similar Big Data technology
• Loves startup culture where everyone's contributions are felt and loved.

About the Role:

As a Product Owner at Abacus Insights, you will play a pivotal role in bringing to market a cutting-edge cloud data platform which enables an industry leading data usability for healthcare payers, ultimately enabling them to turn their vast data into insights that drive improved outcomes. This role requires a robust background in software development or data engineering to effectively translate high-level product vision and customer needs into detailed requirements. You will be working collaboratively with engineering and stakeholders from across the organization to ensure the development of features that optimize value and meet customer needs. As a key member of the product team, you will be responsible for managing Abacus products throughout their lifecycle to include opportunity identification, demand intake, roadmap management, requirement development and analysis, story grooming, design reviews, product acceptance, go-to-market, and sunset planning. This role requires a person who can successfully manage across all aspects of product management while keeping focused on delivering true customer ROI and excelling as a technical translator of business need into technical specifications.

Your day to day: 

• Translate the strategic direction set by the Product Manager into specific product features and oversee development of these features efficiently within the agile framework.
• Support engineers comprehensively throughout all aspects of the Software Development Life Cycle (SDLC), providing guidance, insights, and assistance as necessary.
• Assume end-to-end responsibility for the lifecycle of product features you own
• Act as the primary translator between business needs and technical requirements, effectively bridging the gap to ensure alignment between stakeholders and engineering teams.
• Work with cross-functional teams to analyze current product performance and identify areas for improvement.
• Communicate comprehensive updates, progress reports, and insights related to the product's development to company leadership and other audiences.
• Collaborate with stakeholders, customers, and the development team to gather comprehensive requirements and document customer scenarios, pain points, and success criteria for product feature-sets and features.
• Estimate anticipated impact of each product feature and craft effective value statements to be used in roadmap prioritization.
• Create, prioritize, and manage the product backlog to ensure that the team is working on the most valuable items first.
• Ensure that the development team understands the scope and complexity of the tasks and can accurately estimate the level of development effort.
• Participate in sprint planning, reviews, and retrospectives, ensuring that the development team is building the product as envisioned and meeting the acceptance criteria.
• Act as the primary liaison between stakeholders and the development team, conveying updates, managing expectations, and gathering feedback.
• Ensure visibility, transparency, and clarity of the product backlog for all stakeholders involved.
• Manage product acceptance testing phases to ensure features not only add value but also align with customer needs.
• Coordinate release activities to ensure seamless deployment.
• Prepare relevant documentation and conduct internal user training for new features.
• Collaborate with client management to ennsure they have what they need to support our clients in optimizing the impact of the product.

What you bring to the team:

• A deep understanding of data software engineering or a minimum of 3 years of software development or data engineering experience.
• Domain expertise in shipping Software as a Service (SaaS) products.
• Deep domain expertise in cloud technologies, including AWS, Azure and/or GCP.
• Skilled in database concepts and tools (preferably on Databricks and Snowflake).
• Deep understanding of the data platform R&D processes and agile methodologies.
• Experience with big data, ETL and data pipelines, security and privacy, DevOps, or automation.
• BS or MS in Computer Science, or related fields, or equivalent experience in a technical product owner role or an adjacent position.
• Proven ability to influence and coordinate cross-functional teams to execute plans in highly technical environments.
• Exceptional communication skills critical for translating business requirements into technical specifications, and communicating with technical and non-technical audiences.
• Well-developed strategic-thinking skills, with the ability to inspire and lead others.
• A highly proactive get it done attitude and the skills to back it up.
• Demonstrated ability to thrive in a fast-paced, dynamic startup environment.

What we would like to see, but not required:

• US Healthcare Payor or Provider industry experience.
• Experience with healthcare interoperability standards such as HL7 V2, FHIR and X12.
• Experience with integrating or implementing healthcare-related EMPI/MDM workflows.

What you’ll get in return :

• Competitive Leave & Benefits
• Comprehensive health coverage
• Equity for every employee – share in our success
• Growth-focused environment – your development matters here

Work arrangements :

• Standard hours: 8 hours/day, 5 days/week
• Location: Pune, Hybrid (3 days a week in office)
• Shift: 12pm-9pm IST

About the Role:
As a Senior AI Engineer, you will help architect, build and implement our AI platforms, working closely with data quality engineers, data/software engineers, platform engineers, DevOps, product owners and business.  You provide technical leadership for multiple, diverse, and geographically distributed teams. You will coach and guide teams to enable future success. Also be a partner with engineering managers, directors and VP on strategic initiatives and future business growth opportunities especially around AI and ensuring the engineering organization can scale to meet the need.

As a member of the engineering team, you are responsible for coaching, mentoring, and driving change to enable us to better deliver and innovate in an agile environment.

Your day to day:

• Help shape the direction of our applied AI areas and intelligence features in our products. Drive the deployment of state-of-the-art AI models and systems that directly impacts on the capabilities and performance of Abacus’s products and services (e.g., Abacus Agents, MCP, SQL and RAG agents and tools, etc.)
• Develop novel data collection, fine-tuning, and LLM technologies that achieve optimal performance on specific tasks and domains
• Design and implement ML/AI pipelines for data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation, enabling rapid experimentation, iteration and self-healing
• Work closely with cross-functional teams, including AI engineers, data engineers, and product teams, to deliver impactful AI solutions that enhance user productivity and satisfaction
• Build scalable, reusable backend systems to support GenAI products across the company. Develop robust logging, telemetry, and evaluation harnesses to ensure reliable model and GenAI system performance
• Skillful at driving critical projects (e.g. AI and data engineering Innovation, Development Productivity and Benchmarking)
• Meaningful experience in the world of Data and AI
• Capable of credible customer interactions
• Mentoring development team members to ensure delivered solutions adhere to the software architecture strategy, coding standards, and established organizational policies and procedures
• Participating in software architectural discussions, influencing decisions, and collaborating with peers to maintain consistency across the organization
• Identifying people and process improvement strategies for the Agile/Scrum team(s), especially for AI projects/teams where innovation and iteration are critical
• Communicate organizational updates to ensure teams adhere to the established policies and procedures
• Ensure projects are completed on time and according to our quality standards
• Facilitates communication upward around architecture, design, and implementation objectives
• Proven experience in leading software development teams or projects
• Excellent knowledge of software development design, QA and test automation, and experience with agile development methodologies
• Demonstrated knowledge of Cloud Architecture, AI Architecture, Agents/Agentic systems, Massive Parallel Processing (MPP) compute frameworks for Data+AI platforms, Security, and MCP/API based services
• Good understanding of Incident Management, Configuration Management, Operational efficiency and Customer Escalation Management preferred. Can manage the balance of deliveries on our roadmap commitments while dealing with interruptions and client escalations
• Good experience with open source
• Coach and mentor software and AI developers, develop staff skills, provide continuous feedback 
• Has relentlessly high standards (is never satisfied with the status quo)
• Responsible for protecting, securing, and proper handling of all confidential data held by Abacus to ensure against unauthorized access, improper transmission, and/or unapproved disclosure of information that could result in harm to Abacus or our clients.

What you bring to the team:

• Minimum of 3 years of ML/AI engineering experience in high-velocity, high-growth companies. Alternatively, a strong background in relevant ML/AI research in academia will be considered as contributing qualification
• Minimum of 3 years of experience as a software developer at some point in career
• Minimum of 2 years of experience with Databricks, Mosaic AI Gateway and associated technologies in Databricks stack related to Data and AI engineering
• Strong track record of working with language modeling technologies and GenAI. This could include the following: Developing generative and embedding techniques, modern model architectures, fine tuning / pre-training datasets, evaluation benchmarks, Agents and Agentic workflows (e.g. orchestration, workflow management, observability, debugging), RAG, SQL Agents, MCP, etc.
• Proficiency in Python, TensorFlow/PyTorch, and scalable ML/AI architecture
• Ability to drive end-to-end model and system development, from research and prototyping to deployment and monitoring
• Strong analytical and problem-solving skills, with a passion for improving AI-driven user experiences
• Strong coding and software engineering skills, and familiarity with software engineering principles around testing, code reviews and deployment
• Experience and good understanding of designing scalable, distributed systems for running small to medium scale data processing applications and services (10s – 100s of TBs of data)
• Possesses a level of breadth and depth of software and AI development experience that allows for influence and competence in technical discussions with internal and external stakeholders
• Solid understanding of roles adjacent to the software development (product management, project management, client delivery, operations etc.). Ability to adapt to these roles as defined at Abacus and work with others in these roles
• Proven record of success of driving and/or leading engineering teams/projects towards developing production grade systems supporting customers
• Nice to have experience with, but not required: Snowflake, FHIR, Healthcare Data exposure

What you’ll get in return 

• Competitive Leave & Benefits
• Comprehensive health coverage
• Equity for every employee – share in our success
• Growth-focused environment – your development matters here

Work arrangements 

• Standard hours: 8 hours/day, 5 days/week
• Location: Pune, Hybrid (3 days a week in office)
• Shift: 12pm-9pm IST

About Us

HG Insights is the global leader in technology intelligence, delivering actionable AI driven insights through advanced data science and scalable big data solutions. Our platform informs go-to-market decisions, and we influence how businesses spend millions of marketing and sales budgets.

What You’ll Do:

• Design, build, and optimize large-scale distributed data pipelines for processing billions of events from and to multiple sources of data.
• Architect and scale enterprise-grade big-data systems, including data lakes, ETL/ELT workflows, and syndication platforms for customer-facing products.
• Orchestrate pipelines and workflows with Airflow.
• Write optimized data into analytical databases such as ClickHouse, DuckDB, and Redshift
• Ensure data quality, consistency, and reliability across the pipeline.
• Monitor pipeline performance and troubleshoot data issues.
• Collaborate with product teams to develop features across databases, and backend services.
• Implement cutting-edge solutions for data ingestion, transformation, and analytics.
• Drive system reliability through automation, CI/CD pipelines (Docker, Kubernetes, Terraform), and infrastructure-as-code practices.

What You’ll Be Responsible For

• Development of the Data side of our Platform, ensuring scalability, performance, and cost-efficiency across distributed systems.
• Collaborating in agile workflows (daily stand-ups, sprint planning) to deliver features rapidly while maintaining system stability.
• Ensuring security and compliance across data workflows, including access controls, encryption, and governance policies.

What You’ll Need

• BS/MS/Ph.D. in Computer Science or related field, with 7+ years of experience building production-grade big data systems.
• Strong SQL skills and solid data modeling fundamentals with hands-on experience building ETL / ELT pipelines.
• Proficiency in Python for data processing and integrations.
• Familiarity with dbt, Airflow, Databricks and modern analytics engineering practices

Nice-to-Haves

• Knowledge of data governance frameworks and compliance standards (GDPR, CCPA).
• Contributions to open-source big data projects or published technical blogs/papers.
• DevOps proficiency in monitoring tools (Prometheus, Grafana) and serverless architectures.

What You Will Do and Learn

As a Data Engineer in Data Solutions, you’ll be responsible for designing, implementing, and testing proposed modern analytic solutions.  Working closely with our client partners and architects, you’ll develop relationships with key technical resources while delivering tangible business outcomes. 

Qualifications:

· 2-4 years’ experience developing industry leading business intelligence and analytic solutions

· Must have thorough knowledge of data warehouse concepts and dimensional modelling

· Must have experience in writing advanced SQL

· Must have at least 2+ years of hands-on experience on DBT (Data Build Tool). Mandatory to have most recent hands-on experience on DBT.

· Must have experience working with DBT on one or more of the modern databases like Snowflake / Amazon Redshift / BigQuery / Databricks / etc.

· Hands-on experience with Snowflake would carry higher weightage

· Snowflake SnowPro Core certification would carry higher weightage

· Experience working in AWS, Azure, GCP or similar cloud data platform would be an added advantage

· Hands-on experience on Azure would carry higher weightage

· Must have experience in setting up DBT projects

· Must have experience in understanding / creating / modifying & optimizing YML files within DBT

· Must have experience in implementing and managing data models using DBT, ensuring efficient and scalable data transformations

· Must have experience with various materialization techniques within DBT

· Must have experience in writing & executing DBT Test cases

· Must have experience in setting up DBT environments

· Must have experience in setting up DBT Jobs

· Must have experience with writing DBT Jinja and Macros

· Must have experience in creating DBT Snapshots

· Must have experience in creating & managing incremental models using DBT

· Must have experience with DBT Docs

· Should have a good understanding of DBT Seeds

· Must have experience with DBT Deployment

· Must Experience with architecting data pipelines using DBT, utilizing advanced DBT features

· Proficiency in version control systems and CI/CD

· Must have hands-on experience configuring DBT with one or more version control systems like Azure DevOps / Github / Gitlab / etc.

· Must have experience in PR approval workflow

· Participate in code reviews and best practices for SQL and DBT development

· Experience working with visualization tools such as Tableau, PowerBI, Looker and other similar analytic tools would be an added advantage

· 2+ years of Business Data Analyst experience

· 2+ years of experience writing Business requirements, Use cases and/or user stories, for data warehouse or data mart initiatives.

· Understanding and experience on ETL/ELT is an added advantage

· 2+ years of consulting experience working on project-based delivery using Software Development Life Cycle (SDLC)

· 2+ years of years of experience with relational databases (Postgres, MySQL, SQL Server, Oracle, Teradata etc.)

· 2+ years of experience creating functional test cases and supporting user acceptance testing

· 2+ years of experience in Agile/Kanban/DevOps Delivery

· Outstanding analytical, communication, and interpersonal skills

· Ability to manage projects and teams against planned work

· Responsible for managing the day-to-day client relationship on projects

· Manage the Data engineering lifecycle including research, proof of concepts, architecture, design, development, test, deployment, and maintenance

· Collaborate with team members to design and implement technology that aligns with client business objectives

· Build proof of concepts for a modern analytics stack supporting a variety of Cloud-based Business Systems for potential clients

· Team management experience and ability to manage, mentor and develop talent of assigned junior resources

· Create actionable recommendations based on identified platform, structural and/or logic problems

· Communicate and demonstrate a clear understanding of client business needs, goals, and objectives

· Collaborate with other architects on solution designs and recommendations.