Top Data Engineer Jobs in Boston, MA

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.

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.

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.

Design and build data infrastructure and pipelines using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, collaborate with clients, and apply data architecture, transformation, and analytics to deliver insights.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

The Data Engineer will develop and implement ETL processes, design data architectures, work with data platforms, and ensure data solutions meet scalability and security needs. Responsibilities include mentoring junior engineers and collaborating with stakeholders to gather requirements and optimize data transformations.

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.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

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

Design, build, and maintain scalable cloud-based data pipelines and models (AWS, Snowflake). Implement ETL/ELT, data quality, automated tests, monitoring, and troubleshooting. Collaborate with product owners, data scientists, and engineers to translate business requirements into reliable data solutions and document data flows and technical specifications.

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.

Design, build, optimize, and support an AI-forward data platform. Develop scalable ETL/ELT pipelines using Databricks, Python, and PySpark. Build cloud-native solutions on AWS/Azure, apply CI/CD and secure development practices, collaborate with data scientists and stakeholders, and incorporate AI tools to improve engineering productivity.

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.

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.

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.

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 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.

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.

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

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.

Lead architecture and implementation of scalable batch and streaming ETL/ELT pipelines, data services, and graph-serving models for Observability & Insights. Own technical strategy, drive performance and cost improvements, enforce data governance and observability, lead cross-team initiatives, participate in on-call and incident follow-through, and partner with product and engineering to deliver low-latency graph exploration at extreme scale.

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.