Data Engineer Jobs in Cambridge, MA

Design, build, and support scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, AWS, Databricks, and Snowflake. Develop distributed, real-time, and batch data solutions; apply data modeling, security, encryption, and access controls; enforce reusable engineering patterns; and collaborate with product, software, analytics, and data science teams. The role also involves mentoring, communicating technical concepts to stakeholders, supporting operational resilience, and independently delivering cloud-first data solutions.

Build and architect scalable cloud data platforms, pipelines, and applications. Design distributed, real-time, and batch data solutions using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and operational standards. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring team members and communicating technical outcomes to stakeholders.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Develop distributed data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and operational standards. Collaborate with product, engineering, analytics, and data science teams while mentoring others and communicating technical outcomes to stakeholders. Support resilient, high-performance data workloads and contribute to Agile delivery.

Designs, builds, and maintains secure data pipelines, ETL processes, data warehouses, and quality controls. Collects, validates, cleanses, and analyzes data; develops SQL queries, Tableau dashboards, and AWS-based solutions; investigates data integrity issues; documents standards; and partners with stakeholders to deliver technical solutions using Agile and DevOps practices.

Designs and optimizes AI and machine learning solutions, transforms data into insights, and develops production AI systems. Responsibilities include building data infrastructure, speech recognition and NLP tools, and reusable evaluation and monitoring capabilities. The role independently tests AI systems for performance, robustness, bias, explainability, and failure modes; evaluates technical controls; documents findings; communicates recommendations to clients; and mentors junior team members.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Design and deploy AI systems and machine learning models, perform data wrangling and analysis, and integrate solutions into data infrastructure. Develop scalable AI applications, relational data systems, evaluation tools, automated tests, and monitoring capabilities. Assess AI behavior, robustness, bias, explainability, drift, hallucinations, and guardrails while evaluating technical controls and communicating findings to clients and stakeholders.

Develop and implement AI and machine learning solutions, including algorithms, data models, infrastructure, evaluation tools, automated tests, monitoring, and responsible AI controls. Lead testing for robustness, bias, drift, explainability, hallucinations, and failure modes. Manage client accounts and technical workstreams, review evidence and controls, communicate trade-offs, mentor teams, and maintain delivery and professional standards.

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.

Designs and maintains scalable data pipelines, models, integrations, and ETL/ELT solutions using cloud data technologies. Analyzes complex data, implements quality controls and automated testing, troubleshoots data issues, documents data flows, and supports governance. Collaborates with product owners, stakeholders, architects, data scientists, and engineers in an agile environment while applying AI-assisted development responsibly.

Build and support Databricks Lakehouse pipelines, curated datasets, and analytics-ready data models using Spark, Delta Lake, Python, and SQL. Integrate Databricks with SQL Server, SaaS, and enterprise systems; implement data quality, governance, security, and monitoring practices; optimize Spark workloads; and support CI/CD and platform administration. Collaborate with analysts, data scientists, stakeholders, and Data Enterprise teams to modernize legacy data processes and deliver reliable production data products.

Build and optimize HubSpot environments across multiple clients, including CRM architecture, data governance, workflows, automations, integrations, APIs, webhooks, migrations, reporting data, and troubleshooting. Audit data quality, resolve integrity issues, establish scalable standards, document systems, and improve CRM reliability and performance within an agency or client-services environment.

Build and operate scalable data pipelines and platforms that transform LiDAR, imagery, and GPS sensor data into reliable geospatial deliverables. Responsibilities include automating ingestion, AI processing, quality checks, monitoring, workflows, and data access; partnering with Data Operations and GIS teams; and improving AWS processing performance and cost. The role requires end-to-end ownership, including system design, operation, and on-call support.

Build and operate scalable data pipelines and platforms that transform LiDAR, imagery, and GPS sensor data into reliable geospatial deliverables. Responsibilities include data ingestion, AI processing, quality monitoring, workflow automation, and AWS cost and performance optimization. The role partners with Data Operations, GIS, product, AI, and analytics teams and requires end-to-end ownership, including on-call support.

Build and operate reliable data systems and products supporting clinical operations, drug evaluation, business development, analytics, machine learning, and AI agents. Responsibilities include designing pipelines, canonical data models, data contracts, warehouse transformations, quality and observability practices, governance for regulated data, and incident response. The role partners closely with Product Engineering and Data Science, uses AI-assisted development, contributes to architecture, and mentors other engineers.

Design, build, and maintain mission-critical data and analytics infrastructure on AWS. Develop high-quality code, create system infrastructure, operate distributed systems in production, and contribute to technical design. Review designs and code, establish engineering best practices, and mentor engineers across the organization. Work with large-scale data processing, storage, OLTP and OLAP databases, and real-time analytics systems.

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.

Designs, builds, and maintains scalable data pipelines and warehouse solutions. Responsibilities include data collection, cleansing, validation, quality assurance, ETL, integrity audits, documentation, anomaly resolution, and technical requirements analysis. The engineer develops SQL-based solutions, Tableau reporting, AWS cloud data infrastructure, and automated testing processes while collaborating with customers and Agile/Scaled Agile teams. The role supports data-intensive research, supervision, regulation, payments, and economic analysis across the Federal Reserve System.

Design and scale reliable data platforms, pipelines, Java microservices, APIs, and reporting infrastructure. Build data validation and reconciliation workflows, support distributed systems, improve pipeline performance and resiliency, and partner with engineering, analytics, and business teams. Contribute to cloud-native infrastructure, CI/CD, Docker, Kubernetes, dashboards, and self-service reporting modernization.

Design, build, and operate ingestion pipelines and normalized security data products for vulnerability risk management. Transform raw evidence into model-ready features, enable ownership routing and remediation workflows, and maintain data quality, lineage, and observability. Partner with security, engineering, and platform teams to support reporting, risk models, and shift-left scanning readiness.

Own and evolve Later’s data platform across ingestion, orchestration, storage, transformation, and consumption. Build reliable batch and real-time pipelines, backend services, APIs, infrastructure as code, CI/CD automation, observability, data quality controls, and secure GCP-AWS data movement. Modernize BigQuery and lakehouse infrastructure, improve performance and cost, mentor engineers, and establish governance, metadata, lineage, and freshness foundations for analytics, machine learning, LLMs, and AI agents.

Leads the design and lifecycle management of Oncology R&D data products, including data modeling, architecture, ETL/ELT pipelines, data integration, quality controls, governance, and AI-ready datasets. Partners with scientific, clinical, data science, and IT stakeholders to translate requirements into scalable solutions across clinical, preclinical, real-world, genomic, and omics data. Supports advanced analytics, ML, and GenAI use cases while ensuring interoperability, reliability, compliance, and operational performance.

Design, build, and operate Azure-based data pipelines and integrations. Consolidate data from multiple sources into centralized SQL systems, perform transformations and field mapping, and maintain data warehouse capabilities. Troubleshoot quality, integrity, latency, security, and reliability issues while applying governance, monitoring, and SLA practices. Collaborate with engineers, analysts, and stakeholders in Agile pods to deliver reusable, analytics-ready data assets.

Lead architecture and development of Beacon’s backend data platform, including PostgreSQL data models, Kafka event pipelines, GraphQL APIs, clinical data access controls, and warehouse snapshot pipelines. Design complex systems, tune queries, operate production services, debug cross-system issues, write RFCs, mentor engineers, improve tooling and documentation, and collaborate with scientific, clinical, product, and engineering stakeholders.

Build and oversee reliable ETL/ELT pipelines and data products that transform complex customer data into trusted analyses, visualizations, quality reports, and AI-powered insights. Responsibilities include data exploration, quality control, anomaly detection, AI/ML output evaluation, customer collaboration, documentation, and improving data-cleaning and analytics tools. The role requires cross-functional work in an Agile environment focused on security, data integrity, and clear communication.