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.

Configure and maintain Adobe Experience Platform and Real-Time CDP data pipelines, XDM schemas, datasets, identity resolution, Profile enablement, segmentation readiness, and destination activation. Validate data quality, troubleshoot ingestion and identity issues, manage sandboxes, and implement privacy, consent, governance, and access controls. Partner with architects, data engineers, consultants, analysts, data scientists, and marketing teams to support reporting, personalization, and machine learning readiness.

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.

Design, build, and operate high-throughput data pipelines supporting billing and cost attribution. Drive Spark optimization, latency reduction, sharding, cross-datacenter reliability, and new billing capabilities. Investigate billing accuracy issues with Finance and Product teams, participate in on-call rotation, and improve engineering standards and operational excellence.

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

Design, build, and own the full data platform (ingestion, modeling, CI/CD, orchestration, observability, and BI). Implement streaming and batch pipelines for IoT data, enable governed self-service, enforce data governance/privacy/security, evolve AI access patterns, and provide technical leadership and mentorship to data engineers and data scientists to deliver production-ready analytics and ML capabilities.

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.

The Principal Data Engineer will lead the design and development of scalable data platforms, optimize data pipelines, and collaborate with multiple teams to enhance data solutions.

Supports ETL processes, reports, and data lake operations; monitors production workflows, troubleshoots failures, restarts processes, and resolves issues with internal business partners. The role also creates new processes and reports, provides assignment updates, and participates in scheduled off-hours on-call support. Candidates should have foundational programming, systems administration, testing, performance testing, software development, and DevOps knowledge, plus familiarity with SQL Server and reporting tools.

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.

As a Lead Data Engineer, you will collaborate with Agile teams, develop technical solutions, and enhance cloud-based data warehousing to empower financial services for customers.

Lead design and delivery of scalable data pipelines and APIs for Digital Risk Protection. Mentor engineers, align and ingest multiple data streams (including graph data), productize analytics and LLM outputs, and drive DRP technical projects with stakeholders to enable data-driven intelligence.

Build, test, and support Databricks Lakehouse pipelines using Spark, Delta Lake, Python, and SQL. Develop notebooks, jobs, and reusable components; manage data ingestion and integrations with on-prem SQL Server and SaaS systems. Implement data quality, governance, monitoring, CI/CD, and platform administration while partnering with analysts, data scientists, and stakeholders to deliver analytics-ready datasets.

Design, build, and operate scalable data platforms and pipelines ingesting radar sensor data. Implement ingestion, transformation, enrichment, aggregation, and publishing workflows across relational, NoSQL, time-series, and lake/warehouse systems. Optimize PostgreSQL performance, ensure data quality, observability, and collaborate with engineering, product, and infrastructure teams to enable AI and analytics.

Lead the design and evolution of the enterprise data platform, establish standards, mentor teams, and collaborate across departments to deliver high-quality data products.

Lead Data Engineer responsible for designing, developing, testing, and supporting cloud-based data solutions for Risk Tech. Collaborate with Agile teams to build distributed, real-time data and streaming systems using big data tools, data warehouses, RDBMS/NoSQL, and mentor engineering peers while ensuring performance and code quality.

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.

Design, build, test, and maintain cloud-based data pipelines using Azure, Databricks, Cosmos DB, Delta Lake, and Spark. Develop ETL frameworks, data quality validations, data warehousing solutions, and high-throughput libraries. Troubleshoot pipelines and DevOps deployments, analyze semi-structured data, and contribute across the software development lifecycle within an agile team. The role requires onsite work four days weekly and supports financial-industry products.

Design, develop, and maintain enterprise data pipelines, ETL/ELT workflows, and data management processes in Markit EDM. Integrate market, reference, and transactional data from vendors and APIs, implement data quality checks and reconciliations, support reporting and downstream integrations, troubleshoot production issues, and mentor junior engineers within an agile squad-based team.

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.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.

Own and evolve the data infrastructure powering identity and fraud products. Build and operate scalable ETL/ELT pipelines, ensure data quality and observability, optimize storage and access, support product squads, respond to production incidents, mentor engineers, and drive data platform innovation.

Lead the Data Platform Engineering team owning ETL/reverse-ETL pipelines, replication, and production datastores. Define architecture, roadmap, standards (IaC, observability, schema contracts), mentor engineers, drive incident response and durable fixes, and partner on cross-team technical decisions and operational reliability for data correctness, completeness, and timeliness.