Top Data Engineer Jobs in Boston, MA

Lead design and implementation of data architecture, ETL/ELT pipelines, data models, warehouses and lakes. Ensure data governance, security, cloud deployments, performance and cost optimization. Coach and manage team members, partner with stakeholders, and drive data-driven solutions for clients.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Design and implement data architecture and scalable ETL/ELT pipelines on cloud platforms (AWS/Azure/GCP). Analyze complex problems, mentor junior staff, ensure data governance/security compliance, liaise with stakeholders, and deliver high-quality client solutions while developing technical and client-facing skills.

Lead Data Engineer will design, build, test, and support cloud-based data solutions and streaming systems. Collaborate with Agile teams and product managers, mentor engineers, implement big data tooling (Spark, Kafka, EMR), and optimize data warehousing (Redshift, Snowflake) using Java/Scala/Python and SQL. Ensure code quality with unit tests and reviews.

Design, build, and maintain scalable cloud-based data pipelines and models (AWS, Snowflake). Implement ETL/ELT, data quality checks, automated tests, and monitoring. Collaborate with product owners, data scientists, and stakeholders to translate requirements, perform data profiling and troubleshooting, document data flows, and drive continuous improvements.

The Data Engineer will develop data pipelines, support automation, and collaborate within an Agile team to manage complex data sets, ensuring performance and reliability in data processing.

The Data Engineer will modernize and stabilize the data platform, optimize ELT/ETL pipelines, ensure data quality, and collaborate on AI architecture.

Lead architecture and design of the Beacon Datastore: define scalable data models, build event-driven pipelines, implement features, draft RFCs, coach engineers, and align platform standards to support analytics and clinical workflows.

Build scalable cloud data infrastructure and pipelines to transform transactional and financial data into reporting-ready datasets. Ensure accuracy, traceability, governance, and observability; partner with cross-functional teams, mentor engineers, and leverage AI-assisted tools to improve engineering productivity and enable AI-powered applications.

Design, build, and maintain large-scale ETL/ELT data pipelines and cloud data architectures (AWS/Azure). Optimize Spark/Databricks/Synapse jobs, orchestrate workflows with Airflow, manage SQL/NoSQL databases, implement governance/security, create dashboards, integrate APIs, support ML model pipelines, and deploy using Docker/DevOps practices while monitoring pipeline health.

Lead operational design and engineering for the data platform: own Snowflake and dbt administration, automate platform operations and CI/CD, monitor platform health and observability, enforce governance and access, optimize performance and costs, and develop AI/agent integrations to enable governed data access for business users.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

Lead and mentor a data engineering team to design, build, test, and support cloud-based, real-time data platforms and distributed microservices using modern big data, streaming, and data warehousing technologies to deliver scalable customer and enterprise experiences.

Lead Data Engineer responsible for designing data models, implementing data solutions, managing data pipelines, and collaborating with teams to drive technical initiatives.

Lead design, develop, test, deploy and support cloud-based data engineering solutions. Work across Agile teams on distributed, real-time and batch data platforms, mentor engineers, and optimize code and data pipelines using big data, streaming, and cloud data warehousing technologies.

Build and maintain centralized pricing data infrastructure, scalable workflows, and data pipelines; integrate AI capabilities; deliver pricing tools, reporting, and governance to support data-driven pricing and budgeting across the firm.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Build and maintain governed Silver/Gold data models and semantic assets on Databricks. Translate stakeholder KPIs into testable, reusable transformations, apply enterprise modeling standards, enable domain self-service, ensure data sensitivity and governance, and review domain-contributed models for production readiness.

Build, deploy, and operate production-grade data pipelines and data products for healthcare audiences. Design transformations, data models, and governed views using Python, SQL, Airflow, S3, Snowflake, and EMR. Implement data-quality, monitoring, and privacy-by-design controls for PHI/PII. Partner with product, analytics, and platform teams to onboard sources, support audience discovery, segmentation, activation, measurement, and troubleshoot production issues.

Build, run, and maintain end-to-end data pipelines for mass spectrometry and molecular datasets. Ingest, convert, label, and enrich messy scientific data; implement data quality checks, monitoring, tests, and reproducible builds for ML training and product-read datasets.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

The role involves engineering modern data platforms, focusing on data ingestion, transformation, and storage while utilizing Azure services and MS Fabric, supporting an investment management firm.

Build and maintain scalable, governed data pipelines from ingestion through transformation and integration. Implement schema validation, data quality checks, job monitoring, and production-grade APIs. Work with Python, SQL, Airflow, dbt, AWS (EMR, Athena, S3), Iceberg/Parquet, and contribute to CI/CD, observability, and on-call support while collaborating with analytics and product teams.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

Own product telemetry, maintain dbt models in Snowflake, support self-service reporting and NLP queries, drive product insights from user and retention data, and monitor Airbyte syncs to ensure data quality.