Top Data Engineer Jobs in Boston, MA

The Manufacturing Analytics Engineer will manage the analytics pipeline, transforming operational data from OT systems into actionable insights for decision-making, while collaborating with Engineering, IT, and Operations.

Design, develop, and maintain Snowflake data platforms including schemas, tables, views, streams, tasks, and stored procedures. Optimize SQL and warehouses for performance and cost, implement ingestion/automation (Snowpipe, Streams, Tasks), manage security/RBAC, integrate with cloud and BI tools, leverage Snowflake AI features, troubleshoot production issues, and collaborate with data teams to deliver scalable solutions.

Build and maintain production-grade data pipelines and ELT processes using Python, SQL, Airflow, and dbt within AWS. Design optimized schemas in PostgreSQL and Redshift, leverage AI/LLMs to improve development, automation, testing, and data quality, and scale infrastructure for large healthcare datasets.

Build and maintain large-scale data structures and ETL pipelines using Python and SQL. Design data marts and models, integrate data sources, ensure data quality, and collaborate with cross-functional teams to operationalize analytics on cloud platforms.

Lead design and implementation of a unified event-tracking data platform. Collaborate with Data, Product, and Engineering to build reusable event-publishing frameworks, monitoring/alerting, and deprecate legacy systems. Coach mid-level engineers, influence roadmap and processes, manage cloud/SaaS spend, create documentation, and participate in on-call rotations to ensure platform reliability and data trustworthiness.

Design and build end-to-end geospatial data systems: ingest and process satellite imagery into columnar/chunked stores, expose results via GraphQL/REST and FastAPI, build Next.js/React dashboards, automate with IaC and CI/CD, orchestrate pipelines with Prefect, optimize petabyte-scale I/O, and lead hiring, mentoring, and engineering best practices.

Build and maintain AWS-based ETL pipelines (Glue/PySpark, MWAA/Airflow, Lambda) to ingest and process terabytes of financial data into an S3 data lake using Iceberg/Parquet. Implement metadata, monitoring, tests (90% coverage), materialized views (Trino/Athena), CloudFormation deployments, documentation, and support agile delivery and operations for federal clients.

Build and maintain data marts, materialized views, and pipelines in an AWS Aurora PostgreSQL environment. Integrate data with downstream BI platforms (MicroStrategy), design/optimize workflows for analytics and reporting, monitor pipeline health and data quality, and support federal data security compliance (FISMA/NIST 800-53). Collaborate with engineering and product teams to enable data-driven features.

Senior Addepar data engineer/analyst responsible for supporting and scaling Addepar integrations, building and maintaining data pipelines (Addepar↔Snowflake/Canoe/other platforms), diagnosing and resolving data quality issues, developing SQL-based reconciliations and automation, partnering with business and technology stakeholders, and supporting production operations, governance, testing, and documentation for wealth and asset management data.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

Design, develop, and manage data infrastructure for internal and client projects, focusing on large-scale data pipelines, APIs, and cloud deployments.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.

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.

Design, build, and maintain scalable data pipelines and models; collaborate with analysts and data scientists to provision prepared datasets; implement data quality, governance and monitoring; recommend technologies; lead root-cause analysis, code reviews and testing; mentor teammates and support production data environments.

The role involves managing data infrastructure, building data pipelines, providing analytics to inform business decisions, and shaping AI workflows in the organization.

The Data Engineer role involves developing and maintaining data pipelines for forecasting in commercial real estate, ensuring data integrity and supporting analytics teams.

Develop custom connectors on Airbyte for data ingestion from various platforms, ensuring accurate data integration and transformation. Collaborate with stakeholders and perform testing for reliability.

The Snowflake Data Engineer will work on data cleansing and harmonization, moving from legacy systems to SAP S4, focusing on ETL/ELT development and view creation.

This role involves designing, building, and maintaining data pipelines on Snowflake. Responsibilities include ensuring data quality and collaborating with stakeholders to deliver solutions.

The AWS Data Engineer will work on a data cleansing and harmonization project, focusing on AWS ETL design and development using various AWS services.

The Snowflake Data Engineer will design, build, and maintain data pipelines on Snowflake, optimizing performance and ensuring data quality while collaborating with stakeholders.

The Snowflake Data Engineer and Product Owner will manage the Snowflake data analytics project, collaborating with stakeholders, leading Agile practices, and ensuring quality deliverables through effective team coordination and backlog management.

As a Data Engineer, you will develop solutions for customer-centric technology, ensuring platform integration and managing data architecture. Responsibilities include building data processes, maintaining data integrity, and collaborating cross-functionally to meet business challenges.

The Principal Data Engineer designs and maintains scalable data systems, evaluates new technologies, mentors junior engineers, and leads data engineering initiatives in agile environments.

Designs, builds, and maintains scalable ETL/ELT data pipelines and integrations. Profiles and cleanses data, creates mappings and SQL, documents metadata, monitors data quality, participates in code reviews, and collaborates with analytics, DBAs, and business partners to enable accurate analytics and data warehousing.