Data Engineer Jobs in Cambridge, MA

The role involves engineering modern data platforms, focusing on data ingestion, transformation, and storage while utilizing Azure services and MS Fabric, supporting an investment management firm.

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 on-site preclinical data management: collect, map, clean, validate, and standardize heterogeneous bench and lab data; load into a central data lake; support users, troubleshoot systems, implement data standards, perform testing/validation, and produce BI extracts and reports.

Design, build, and operate the core data warehouse, ingestion, orchestration, and cataloging platform. Develop batch and streaming pipelines, ensure data quality, governance, observability, and provide ML data platform capabilities. Lead architecture, improve platform reliability and performance, and collaborate with product, engineering, data science, security, and compliance teams.

The ML Data Engineer develops and maintains machine learning pipelines and systems, collaborating with teams to optimize manufacturing processes through data-driven insights and predictive models.

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.

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.

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.

Lead design and implementation of high-scale, multi-tenant AI inference data plane services. Optimize distributed LLM serving (prefill/decode disaggregation, KV-cache routing, autoscaling, flow control), contribute upstream to open-source inference projects, mentor engineers, and operate resilient, observable production services running on Kubernetes and specialized AI hardware.

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.

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.

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.

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.

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.

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.

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.

Design, build, and maintain scalable ETL/ELT pipelines and Snowflake data models. Integrate APIs and event streams, orchestrate workflows (Airflow/Prefect/Dagster), implement data quality, lineage, and observability, and apply DataOps/DevOps practices (Git, Docker, CI/CD, IaC).

The Engineer will operationalize scientific tools and methods within the Data Foundry, building deployment pipelines, model serving infrastructure, and ensuring APIs are robust and analytics-ready for both scientists and AI agents.

The Automation Engineer designs, develops, and maintains data pipelines and API integrations for scientific workflows while ensuring compliance with industry standards. The role involves collaboration with scientists and the use of various technologies to optimize data processes.

The role involves building and maintaining scalable data infrastructure, designing data pipelines, and collaborating with various teams to optimize data processing. Responsibilities include ensuring system reliability and mentoring other engineers.

The Senior Software Engineer will design and implement software solutions for investment processes, engage with leaders, automate development, and support production escalations.

Lead architecture and development of Beacon's centralized Datastore, designing scalable data models, event-driven pipelines, and APIs. Coach engineers, draft RFCs, set platform standards, and collaborate with product and scientific teams to ensure data integrity, maintainability, and operational reliability for clinical-grade brain and physiological data.

Design, configure, and tune DLP controls across email, print, web, removable media and data-at-rest. Analyze DLP events, implement preventative controls, create dashboards and reports, integrate continuous security event ingestion with Splunk, and perform insider risk data mapping and remediation across cloud and network storage.

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