Top Senior Data Engineer Jobs in Boston, MA

Leads payer healthcare data engineering and analytics initiatives, designing pipelines, data models, and interoperability frameworks for claims, membership, and provider data. Directs large technical programs, advises clients on data architecture and modernization, validates data quality and HIPAA controls, applies statistical and machine learning methods, coaches technical teams, and presents actionable recommendations to stakeholders.

Designs and maintains reusable data ingestion and transformation components, automated quality and governance checks, reliable pipelines, monitoring, deployment automation, cloud integrations, containers, and infrastructure as code. Partners with data consumers on usable models and datasets, documents standards and runbooks, supports Agile improvement, and responds to critical IT incidents. Requires 5+ years of data engineering experience, orchestration, distributed processing, storage, scripting, automation, and version control expertise.

Build and architect scalable cloud data platforms, pipelines, and applications using technologies such as Python, Spark, Databricks, and Snowflake. Lead large-scale data initiatives, make architectural decisions, establish engineering patterns, and ensure platform resilience and operational efficiency. Collaborate with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical concepts to stakeholders. Evaluate data platform choices and deliver solutions supporting machine learning, distributed systems, and evolving enterprise data needs.

Lead large-scale data engineering initiatives, designing scalable cloud data platforms, pipelines, and architectures. Use Python, Spark, SQL, Databricks, Snowflake, AWS, relational and NoSQL databases to deliver resilient data solutions. Mentor engineers, analysts, and data scientists; establish engineering standards; evaluate platform choices; and collaborate with product and software teams to support enterprise data transformation and AI capabilities.

Designs reusable data ingestion and transformation components, automates data quality, governance, testing, lineage, and deployment workflows, and improves pipeline observability and reliability. The role supports monitoring, incident response, cloud and container integrations, infrastructure as code, documentation, reusable datasets, and Agile platform improvements. Candidates need five or more years of data engineering experience plus expertise in orchestration, distributed processing, storage, scripting, automation, and version control.

Build and operate scalable data pipelines and models on Snowflake and AWS for Global Liquid Markets investment products. Responsibilities include integrating market and vendor data, implementing automated testing and governance standards, developing AI-powered engineering workflows, collaborating with product and business stakeholders, contributing to the data roadmap, mentoring engineers, and participating in code reviews. The role requires strong communication, architecture, agile delivery, data engineering, and responsible AI skills.

Design and optimize Databricks ETL/ELT pipelines on Azure using PySpark, SQL, Delta Lake, and Kafka. Build CDC and near-real-time processing solutions, ingest data from diverse sources, implement data quality controls, and apply SCD Type 1 and Type 2 modeling. Automate workflows with Databricks Workflows and GitHub-based CI/CD while collaborating in an Agile environment.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Build and maintain production data pipelines, dbt models, Airflow workflows, and governed datasets supporting People Analytics. Integrate Workday and other HR systems into reliable Snowflake data products, implement data quality monitoring, row-level security, semantic models, and AI agents. Partner with engineers, analysts, data scientists, and business stakeholders to deliver trusted data solutions, communicate technical trade-offs, enforce governance, and mentor other engineers.

Develop and lead scalable cloud data engineering solutions for data warehouse migration, modernization, analytics, and ETL/ELT projects. Design and implement solutions using Google Cloud technologies, SQL, Python, and BigQuery. Create testing strategies and plans, collaborate with customers and development teams, translate business requirements into technical solutions, and contribute to an agile environment.

Designs, builds, and optimizes scalable data pipelines, ETL workflows, integrations, and reporting solutions. Develops SQL, Python, and Spark processes; supports Azure and Microsoft Fabric data platforms; monitors production reliability; collaborates with stakeholders; participates in code reviews and documentation; and mentors junior engineers.

Designs, develops, and maintains scalable data systems, pipelines, ETL processes, databases, and data warehouses supporting public health operations. Responsibilities include data integration, cleansing, transformation, governance, security, monitoring, optimization, and collaboration with analysts, data scientists, and stakeholders. The role also provides technical guidance, training, and mentorship while supporting sustainable data infrastructure and traveling domestically up to 10%.

Build and maintain scalable ETL/ELT pipelines, integrate data from APIs, databases, and third-party systems, and develop reliable data models for analytics and reporting. Ensure data quality, governance, security, and accessibility while supporting batch and real-time processing. Monitor pipeline performance, troubleshoot failures, and collaborate with Product, Analytics, Engineering, and Cloud teams to improve data architecture and enable data-driven decisions.

Build and operate scalable data pipelines, governed data models, semantic layers, and analytics products. Partner directly with business stakeholders to translate requirements into production-ready data and AI solutions. Develop SQL and Python applications, LLM and agentic workflows, retrieval systems, and automated testing. Ensure data quality, lineage, monitoring, reliability, cost efficiency, and compliance for PHI and genomic data. Collaborate with platform and AI teams on architecture, governance, CI/CD, and operational excellence.

Leads a small data engineering team while contributing hands-on development. Responsibilities include designing and maintaining dbt and Python ETL pipelines, managing data quality and UAT, coordinating implementation timelines, conducting code reviews, mentoring engineers, documenting systems, and supporting project delivery. The role also involves building and deploying AI agents for data quality, pipeline monitoring, and analytics workflows, with a focus on healthcare claims data and large-scale datasets.

Lead the design and evolution of BJ’s Databricks Lakehouse, building batch and real-time pipelines, integrating digital and operational data, and establishing data quality, governance, CI/CD, and cost optimization. Develop AI-powered analytics and Databricks agents, own the digital data roadmap, and mentor a distributed engineering team while partnering with product and business leaders.

Designs, builds, and optimizes enterprise Databricks Lakehouse pipelines and data products using Spark, Delta Lake, SQL, and Python. Leads complex data initiatives, production support, governance, CI/CD, workflow orchestration, monitoring, and modernization of legacy SQL Server solutions. Partners with stakeholders, optimizes performance and cost, mentors engineers, and drives engineering standards across Azure data platforms.

Lead the reverse-engineering and migration of legacy ETL pipelines from SAP BusinessObjects Data Services and JD Edwards into dbt and Databricks architectures. Build automated data validation, maintain SQL Server write-back mechanisms, collaborate with client stakeholders, and lead and mentor a small engineering team. The role requires hands-on expertise in SQL, Python, Spark, orchestration, cloud platforms, and legacy ERP and ETL migrations.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Lead high-severity incident response and build data pipelines for operational reliability reporting. Analyze incident trends, identify recurring failures and customer-impacting services, and provide prioritized recommendations to engineering leadership. Facilitate postmortems, operational health forums, runbook improvements, and corrective-action tracking. The role requires strong SQL and Python skills, incident-management platform experience, dashboarding expertise, and calm communication during critical incidents.

The AVP, Data Engineer will design and build data ingestion, transformation, and replication pipelines using SQL, Python, dbt, and Dagster. Responsibilities include translating requirements into technical designs, developing data products, debugging production issues, supporting testing, and improving data engineering practices. The role also involves data modeling, mentoring junior engineers, conducting code reviews, and providing technical leadership while remaining hands-on. Experience with modern data platforms, workflow orchestration, cloud ecosystems, and insurance or financial services is preferred.

Leads data architecture and engineering modernization across business domains. Designs and implements complex pipelines, integrations, data models, and orchestration using Snowflake, AWS, and Airflow. Establishes data quality, governance, security, testing, observability, CI/CD, and reliability practices. Provides architectural guidance, evaluates technical solutions, leads cross-team initiatives, mentors engineers, and improves platform performance and cost efficiency.

Build and maintain scalable Snowflake-based ELT/ETL pipelines, production data models, and observability frameworks. Translate business requirements into reliable solutions supporting analytics, BI, and ML. Establish data quality, governance, lineage, security, and compliance practices while optimizing warehouse performance, cost, and data freshness. Collaborate with Analytics, Product, and Engineering teams, contribute to architecture decisions, evaluate Snowflake tooling, and use AI-assisted development tools to accelerate delivery.

Designs, builds, and optimizes scalable data collection, storage, access, and analytics systems. Creates and automates reliable data pipelines, supports data quality and governance, ensures SLA and performance compliance, and monitors pipeline operations. The role requires collaboration with analytics, business, and customer teams, with potential work involving AI/ML pipelines, Databricks, DevOps practices, streaming data, and event-driven architectures.

Own the lab data platform from instruments, ELN/LIMS, and bench workflows through BigQuery, Kernel, and KernelAI. Build ingestion pipelines, schemas, APIs, quality checks, lineage, versioning, ML-ready datasets, reporting tools, and dashboards. Partner closely with biotech scientists and computational teams to standardize experimental data and support biological modeling and design.