Data Engineer Jobs in Cambridge, MA

Designs, builds, administers, and supports cloud data architectures, databases, schemas, metadata repositories, ETL processes, and analytic models. Leads data quality, governance, troubleshooting, testing, automation, reporting, dashboards, and infrastructure design. Collaborates with Agile teams to translate business requirements into technical capabilities and supports decision-making for Audit and business management. The role is full-time onsite at an eligible Federal Reserve location and requires extensive security screening.

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.

The Data Engineer will design and build data solutions, develop ELT jobs, ensure data quality, and collaborate with technical teams to align data strategies with business goals.

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.

Leads the design, development, and ownership of scalable data systems, ELT pipelines, data models, and Snowflake architectures supporting analytics, experimentation, machine learning, and business decisions. Partners cross-functionally to deliver reliable, governed data solutions; improves quality, observability, testing, documentation, and operational practices; mentors engineers; and shapes technical direction through automation, technology evaluation, and AI-enabled engineering.

Build and own trusted data products, dimensional models, semantic layers, and dbt transformations that support analytics, experimentation, machine learning, reporting, and decision-making. Partner with cross-functional teams to translate business needs into scalable datasets, improve data quality and governance, establish engineering standards, and drive self-service analytics. Mentor engineers and analysts while leveraging AI tools to improve development, documentation, and quality.

Design, build, and operate WHOOP’s data platform infrastructure, including lakehouse, warehouse, and streaming systems. Improve performance, reliability, cost efficiency, observability, and developer experience across AWS-based data systems. Develop internal tools, APIs, automation, CI/CD, and infrastructure-as-code practices. Partner with data, ML, analytics, and software engineering teams to support scalable workflows, governance, and production excellence while using AI-assisted development tools effectively.

Designs, builds, administers, and troubleshoots cloud data architectures, databases, schemas, metadata repositories, ETL processes, and analytic models. Ensures data integrity and quality, develops SQL queries and reports, leads testing and automation improvements, and supports dashboards, governance, data sharing, and metric collection. Collaborates with Agile teams to translate business requirements into technical infrastructure designs and decision-support capabilities using AWS, Java, Python or Scala, GitLab, and Terraform.

Develop and maintain data-protection features in CrowdStrike Falcon sensor on Windows, working across kernel and user mode. Implement filesystem minifilter drivers, AI-aware detection/protection logic, debug kernel issues, own features end-to-end, and collaborate with cross-functional teams to ship and support endpoint data security capabilities.

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.

Build and operate AI-enabled, production-grade healthcare data platforms: design scalable pipelines, model and standardize data in Databricks, write Python/SQL/Java for reconciliation and validation, enable LLM-powered data access, implement monitoring/governance, and collaborate with vendors and stakeholders.

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.

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.

Lead cross-team data platform initiatives focused on the Data Lake. Drive architecture and roadmaps for lakehouse storage, compute, orchestration, modeled data layers, and operational automation. Own large, complex programs end-to-end, make high-judgment architectural decisions, establish standards and paved paths, respond to incidents, and mentor senior engineers while aligning stakeholders across engineering, product, analytics, and AI/ML.

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.

Own the direct sales motion for CarGurus data products: prospect and qualify buyers, run consultative enterprise sales, scope data assets and delivery, close deals, and coordinate delivery. Drive dealer monetization, generate customer intelligence, and feed market learnings to Product, Data, and Strategy teams to inform pricing and roadmap.

Lead design and delivery of Klaviyo's scalable data platform (streaming, batch, lakehouse, governance). Define SLOs, author ADRs/RFCs, implement ingestion/CDC, processing, storage, catalog/lineage and access controls. Optimize performance/cost, apply ML/GenAI to data workflows, partner with product/analytics, contribute code/reviews, and mentor senior engineers to enable reliable, multi‑tenant data at 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.

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 technical strategy and implementation of V-BAT fleet data analysis pipelines and tooling. Transform raw flight, simulation, and production data into metrics, dashboards, and reports; perform fleet-wide studies, anomaly investigations, and root-cause analysis; partner with cross-functional teams and mentor engineers on analysis best practices.

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

Build and maintain R&D data infrastructure for experimental, manufacturing, and QC data. Implement DAQ integrations, databases, validation checks, analysis pipelines, visualizations/dashboards, and perform statistical/regression analyses to support cross-functional technical teams.

The Data Engineer designs and builds data pipelines for analytics, ensuring data quality, governance, and integration across systems, while collaborating with teams for effective delivery.