Top Data Engineer Jobs in Boston, MA

Build and operate ingestion and transformation pipelines to turn messy insurance data into clean datasets. Design bronze/silver/fact layers, model and tune Postgres and analytical stores, define KPIs and reconciliation checks, orchestrate observable data jobs, and ensure end-to-end data quality for analytics and AI products.

Build and maintain data marts, materialized views, and ETL/ELT pipelines in an AWS-hosted data lake. Design and optimize data workflows, monitor infrastructure health and data quality, support PostgreSQL migrations, collaborate with cross-functional teams, and ensure compliance with federal data security (FISMA/NIST 800-53).

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Lead architecture, systems, and roadmap for Coinbase's Compliance Reporting Data Platform. Design and build production-grade data models, pipelines, quality frameworks, validations, and certification processes. Establish data contracts, monitoring, reconciliation, and audit-ready processes. Partner with engineering and compliance stakeholders, respond to regulatory exams, and mentor engineers while shaping standards and documentation.

The Data Engineer will design and maintain data pipelines, enhance the data warehouse, support machine learning applications, and collaborate with various teams to leverage data effectively.

As a Data Engineer, you will build and maintain data pipelines, convert data into metrics, and operate the platform on AWS. Responsibilities include writing production code, processing data from APIs, and enhancing the orchestration layer using Dagster, while ensuring compliance in a HIPAA-regulated environment.

As a Data Engineer, you will build and maintain data pipelines, ensure data quality, support ML workflows, and optimize query performance using various technologies.

Build and maintain scalable data pipelines and integrations using Databricks and Azure. Transform and model large structured and unstructured datasets to support reporting, analytics, and operational needs. Implement data quality, monitoring, and governance; troubleshoot pipeline performance; collaborate with stakeholders and mentor junior engineers.

The Data Engineer is responsible for designing data workflows and pipelines, implementing systems engineering principles, and deploying data streaming technologies while ensuring compliance with industry regulations and best practices.

Forward-deployed senior data engineer who deploys, hardens, integrates, and operates a data platform (lakehouse, streaming, governance) in DoD environments. Build pipelines across classification boundaries, produce accreditation evidence (STIG/cATO/FIPS), run day-2 operations, train mission teams, and mentor junior FDEs while feeding field feedback into product improvements.

The Forward Deployed Data Engineer will manage AI-automated data platform migration projects, ensuring timely delivery and communication with clients while utilizing proprietary tools.

The Data Engineer I at Storable supports the design and maintenance of data pipelines, works with ETL processes, optimizes workflows, and collaborates with various teams to enhance data accessibility and reliability.

Design, build, and maintain scalable batch and near-real-time data pipelines and data products using Databricks and Spark. Integrate internal/external sources, implement data models, ensure data quality, support BI (Sisense), CI/CD, and collaborate with analysts, scientists, and stakeholders to enable analytics and decision-making.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

Design, build, and maintain scalable ETL and data pipelines on AWS using Python/PySpark. Develop Glue/Lambda/Step Function workflows, event-driven integrations, and data lake/warehouse solutions. Tune SQL, implement monitoring/validation, support API/JSON integrations, troubleshoot operations, document and automate deployments, and mentor junior engineers.

Support building and maintaining ETL pipelines using Python and PySpark. Work with AWS services (Glue, Lambda, Step Functions, S3, Redshift, RDS, EMR) to ingest, transform, and validate data. Write complex SQL, implement basic monitoring/logging, process API/JSON payloads, and collaborate with engineers, analysts, and stakeholders on deployment and documentation.

Design and implement scalable data pipelines and orchestration on GCP to support AI/ML-driven mail intelligence. Build stream and batch processing, ensure data governance and quality, optimize workflows, create visualizations, and collaborate with US cross-functional teams to align technical strategy and recover from failures.

Design, develop, maintain, and optimize ADABAS database structures and data solutions. Support mainframe data processing (Natural, COBOL, JCL), perform migrations to modern platforms, tune performance, ensure data integrity/security, troubleshoot issues, and collaborate with developers, architects, and analysts on modernization, API/cloud integration, and data warehousing initiatives.

Design, build, and maintain high-throughput data processing services, focusing on streaming and batch data systems to enhance player experiences.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Build and own R&D data infrastructure for lab and pilot hardware systems: acquire and validate time-series/sensor data, design schemas, create pipelines, analyses, and dashboards, and partner with scientists and engineers to support statistical/regression studies and QC/manufacturing insight.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Design, build, and maintain Data Vault 2.0 data warehouse and Power BI solutions. Develop ETL pipelines, data models, reusable datasets, and data governance. Perform profiling, cleansing, migrations, and collaborate with infrastructure teams to optimize data delivery, automation, and reporting.