Top Data Engineer Jobs in Boston, MA

Leads complex data engineering initiatives for global cybersecurity analytics. Designs data architectures, ingestion pipelines, governance standards, and quality processes; collects and transforms large datasets; develops Power BI visualizations; improves data reliability and accessibility; documents systems; advises stakeholders; mentors team members; and responsibly applies AI tools to improve workflows while protecting confidential data.

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.

Build and operate production data pipelines and dimensional models using Spark, SparkSQL, and cloud lakehouse technologies. Own pipelines from requirements through deployment, monitoring, and iteration; improve data quality, lineage, reliability, and cost efficiency. Partner with data scientists, analysts, product managers, and engineers to support datamarts, KPIs, reporting, and analysis. Participate in business-hours on-call rotations and improve runbooks and alerting.

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

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.

Define and govern enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, and AI use cases. Establish standards for data quality, lineage, access, cataloging, governance, observability, and SLAs. Architect AI-enabled workflows, resolve complex architecture issues, influence roadmaps, and mentor engineers through hands-on technical leadership. The role requires 15+ years of software, data engineering, or architecture experience and expertise in large-scale data platforms and modeling.

Build and scale data insights capabilities, write SQL queries in Snowflake, transform data with Python, and create dashboards in Qlik Sense and Tableau. Support data governance, preprocessing pipelines, predictive modeling, documentation, version control, and automated data workflows. Collaborate with Data Engineering teams to deliver real-time insights for robotics and supply chain automation projects.

Build and own PFA’s data platform, including healthcare data integrations, ingestion pipelines, data models, quality processes, reporting layers, and analytical infrastructure. Develop production-grade Python and SQL systems, predictive models, AI-enabled workflows, and executive dashboards. Establish engineering standards, testing, governance, and scalable architecture while translating business needs into technical solutions and communicating insights to nontechnical leaders.

Designs and maintains Snowflake-based cloud data pipelines and lake architectures, develops scalable ETL/ELT workflows, and supports legacy-platform migrations. Ensures data cleaning, reconciliation, validation, and financial data integrity. Translates business requirements into Jira epics and user stories, supports project planning, testing, cutover, and go-live readiness, and coordinates with business, finance, operations, and technical stakeholders.

Design, develop, test, deploy, and optimize high-throughput streaming, event-driven, and batch data pipelines using Databricks, Scala/PySpark, and cloud infrastructure. Build data lakehouse solutions, troubleshoot DevOps and Azure issues, perform root-cause and impact analysis, and support the full software development lifecycle. Collaborate with distributed stakeholders, drive technical improvements through proofs of concept, and maintain scalable data integration and curation solutions.

Build and maintain production ETL/ELT pipelines, data models, and curated datasets for analytics and AI applications. Ingest data from databases, APIs, SaaS tools, and event streams; implement quality checks, monitoring, documentation, governance, and access controls. Collaborate with analysts, data scientists, and ML engineers on reporting, feature datasets, AI search, and RAG applications. Optimize query performance and pipeline costs while using Git, code reviews, and CI/CD for safe releases.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Build and operate event-driven data pipelines processing healthcare claims data for fraud, waste, and abuse detection. Operationalize validated fraud signals, maintain alert delivery and partner dashboards, monitor pipeline reliability, and collaborate with data architects, scientists, and investigative teams. The role requires production-scale pipeline engineering using AWS, Snowflake, Python, and SQL in a fully remote environment.

Own and evolve UJET’s data platform, including MySQL CDC replication to BigQuery, dbt transformations, Terraform infrastructure, pipelines, data quality, observability, and Looker models. Build reliable, analytics-ready datasets and trusted metrics for internal and customer-facing reporting. Partner with Analytics, Finance, Product, and Engineering to deliver scalable data solutions, improve platform reliability, optimize performance and costs, and establish development standards and guardrails.

Designs and maintains end-to-end data solutions, including ETL/ELT pipelines, BigQuery platforms, APIs, semantic data layers, and data catalogs. Builds data quality monitoring, governance, security, and access controls while collaborating with analytics, product, engineering, and business teams. Requires strong Python, Kotlin, Java, SQL, API development, cloud data engineering, testing, and reliability experience.

Build and maintain production data pipelines and foundational datasets for Market Intelligence. Automate survey ingestion, cleaning, joining, scheduling, monitoring, and reporting-layer integration. Develop repeatable queries, improve data quality practices, document assumptions, support reproducible outputs, and translate research needs into technical solutions. The role requires close collaboration with technical and nontechnical stakeholders and ownership of reliable data infrastructure.

Build and operate platform infrastructure for product analytics, data processing, and ML systems. Responsibilities include event tracking pipelines, Databricks Bronze-to-Gold lakehouse layers, schema evolution, data contracts, CI validation, backfills, monitoring, feature computation, model pipelines, lineage, PHI policy enforcement, and self-service developer tooling. The role focuses on making data and ML changes safe, observable, reproducible, and accessible to engineers and subject-matter experts.

Design, build, and scale data pipelines, ETL workflows, analytics infrastructure, and machine-learning data systems. Ensure data quality, security, monitoring, and reliability while optimizing storage and processing performance. Collaborate with engineering, product, and analytics teams on data requirements, modeling, schemas, and scalable infrastructure. The role also establishes data engineering best practices and supports a data-driven culture.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Build and maintain data pipelines, governance documentation, and data architectures supporting Department of State systems. Collect, clean, transform, integrate, validate, and analyze data from multiple sources using Palantir Foundry and relational databases. Collaborate with nontechnical stakeholders to gather requirements, identify data-quality issues, develop reporting tools and visualizations, and recommend improvements supporting data-driven decisions.

Designs and builds cloud-based, data-centric applications supporting clinical and operational healthcare processes. Develops data pipelines, transformations, enrichment processes, provisioning layers, and user interfaces. Collaborates with Product, Platform, and Architecture teams using modern software development practices, source control, documentation, and regular delivery methods.

Designs, builds, tests, deploys, and maintains secure ETL/ELT pipelines for public health data. Integrates Power Platform, Azure services, APIs, SQL Server, CDC/NIOSH systems, and approved file-transfer platforms. Develops batch, event-driven, and near-real-time data workflows supporting intake, validation, reporting, analytics, notifications, and results delivery while meeting FISMA Moderate, federal privacy, Agile, and DevSecOps requirements.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.