Data Engineer Jobs in Cambridge, MA

Architect and maintain secure cloud data infrastructure, scalable schemas, and resilient batch and streaming pipelines. Deploy data platforms using Terraform, Helm, CI/CD, and infrastructure-as-code practices. Build monitoring and alerting for data health, system performance, and container observability. Troubleshoot application workflows, database queries, and pipeline logic while advancing data governance. Support MLOps through AI model versioning, containerization, and deployment.

Designs, develops, and maintains IBM Planning Analytics and TM1 models, cubes, applications, rules, feeders, and TurboIntegrator processes for financial planning, forecasting, reporting, and actuarial modeling. Builds data ingestion, transformation, quality, masking, and curation processes; supports SQL development, security, performance monitoring, and critical data pipelines. Partners with finance and actuarial stakeholders, provides operational support, develops scalable tested code, and mentors team members.

Build scalable data infrastructure, pipelines, streaming solutions, and governed data models supporting regulatory reporting, financial operations, analytics, and AI-powered applications. Partner with Engineering, Compliance, Analytics, and business teams to deliver accurate, auditable data products. Improve data quality, observability, platform reliability, and engineering productivity while mentoring teammates and contributing to modern cloud data platform strategy.

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.

The Senior Data Engineer will design, implement, and manage data pipelines for real-time analytics and ML training datasets, ensuring efficient data flow and quality in a hybrid environment.

Leads the design, engineering, and commissioning of high-voltage electrical infrastructure for hyperscale data centers. Responsibilities include substation and power distribution design, utility interconnection, electrical system studies, redundant power architectures, generation and energy storage integration, technical leadership, design reviews, construction support, and client advisory work.

Build and operate scalable data infrastructure, including fault-tolerant pipelines, data discovery services, and data lineage systems. Develop backend services that process petabytes of data for machine learning and autonomy teams. Lead system design, code reviews, and technical contributions while using AWS cloud ETL technologies and distributed systems. Collaborate with stakeholders to improve platform adoption and address data customer needs.

Designs, builds, and maintains scalable Azure data pipelines and ELT frameworks using Databricks, SQL, Python, and PySpark. Implements Azure DevOps CI/CD, monitoring, security, infrastructure automation, and production support. Collaborates with BI teams on Power BI datasets, documents data solutions, participates in Agile delivery, and mentors junior engineers. The role requires strong data modeling, structured and semi-structured data expertise, performance tuning, and data governance awareness.

Create and quality-assure high-fidelity mechanical engineering datasets for AI model training, evaluation, and customer delivery. Build CAD reference models, assemblies, annotations, exploded views, sequences, and manufacturing artifacts. Develop data workflows, metadata standards, quality checks, and documentation while collaborating with product, research, engineering, and customer-facing teams. Translate mechanical engineering, CAD, manufacturing, DFM, and customer data requirements into structured datasets and scalable processes.

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 and operate reliable data pipelines transforming flight test, simulation, and operational data into versioned, traceable ML training datasets. Responsibilities include ingestion, normalization, time alignment, validation, dataset lineage, anomaly mining, labeling workflows, deployment feedback loops, monitoring, and data coverage analysis. Collaborate with flight test and operations teams on instrumentation and data access. The role is on-site in Boston and requires production data engineering experience supporting machine learning pipelines.

Build and productionize data pipelines and LLM-based, multimodal AI solutions for insurance use cases. Develop agentic AI applications using RAG, embeddings, prompting, custom tools, and evaluation frameworks. Apply responsible-AI practices, deploy and monitor machine learning systems, and lead client workshops to translate business needs into technical solutions and roadmap priorities.

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.

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.

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 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.

Leads the design, implementation, and optimization of cloud-native data infrastructure, platforms, storage solutions, and complex data pipelines. Establishes engineering standards for testing, monitoring, releases, and reliability; troubleshoots cross-system failures and leads incident reviews. Partners with infrastructure, laboratory IT, data science, bioinformatics, and scientific teams to deliver secure, scalable solutions. Mentors engineers, guides technical initiatives, and uses generative AI to accelerate development and data transformation.

Designs and enhances an enterprise data and AI platform, building reusable AI starter kits, reference implementations, templates, and developer-focused patterns. Partners with engineering, governance, and product teams to embed governance, accelerate AI adoption, and reduce delivery time. Leads enablement programs, champions cloud-first innovation, advises cross-functional initiatives, and mentors teams developing and deploying AI solutions. Requires expertise in AI/ML, GenAI, cloud data products, Snowflake or Databricks, agile delivery, and enterprise software development.

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 Senior Data Engineer will build data pipelines and infrastructure, modernize legacy systems, and ensure data reliability for analytics at ABCorp.

The Data Engineer will develop and optimize data pipelines for LLMs, ensuring high-quality healthcare datasets while collaborating with data scientists on model requirements.

Drive profitable revenue growth for industrial services serving data center customers. Develop market strategies, identify and close opportunities, negotiate pricing, manage assigned accounts, maintain sales records, support customer appointments, coordinate with sales and operations teams, and manage sales expenses. The role requires relationship building, customer support, and travel up to 50% while working remotely.

Drives profitable revenue growth for data center and industrial services accounts. Develops market strategies, identifies and negotiates sales opportunities, analyzes customer requirements, maintains account information, builds customer relationships, supports major job scopes, collaborates with sales and operations teams, coordinates customer support, and manages sales expenses. Travel up to 50% is required, with remote work available.