Top Data Engineer Jobs in Boston, MA

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 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.

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.

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.

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.

Staff Data Engineer will set technical direction and build Eve’s scalable data platform. Responsibilities include Snowflake administration, role-based access, governance, ingestion, dbt modeling and orchestration, Terraform infrastructure, observability, schema change detection, reliability practices, and CI standards. The role also mentors engineers and analysts, supports AI-assisted development, and establishes secure, reliable workflows for product, case, customer, and go-to-market data.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Build and maintain Databricks-based data platforms, including ingestion, transformation, storage, governance, data modeling, and serving pipelines. Establish medallion architecture standards, canonical data models, quality controls, lineage, schema evolution, and reliable batch or incremental processing. Improve pipeline observability, scalability, idempotency, and recoverability while moving curated data to systems such as ClickHouse. Collaborate across application and analytics teams to create durable, governed production datasets.

Own Double E’s data lake and improve data integrity by building ETL pipelines, data models, integrations, and reporting solutions. Manage Azure SQL and Power BI, consolidate ERP and CRM data across global offices, establish governance and documentation standards, and translate business needs into reliable, scalable data infrastructure. The role partners with technical and business stakeholders and supports modernization, system connectivity, and analytics initiatives.

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.

Build and operate ETL and real-time pipelines connecting veterinary practice systems, billing platforms, and third-party APIs. Design data models and a warehouse supporting hospital analytics, finance, and reporting. Establish standards for testing, documentation, monitoring, and data quality, while troubleshooting production data issues. Partner with Finance to improve Sigma self-service reporting and define scalable integration patterns as the company’s first data engineer.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Design, build, and maintain scalable batch and streaming data pipelines using Python, SQL, Kafka, and GCP. Develop REST and GraphQL data services, CI/CD automation, observability, and data-quality frameworks. Integrate ML and NLP models into production workflows, including continuous retraining and real-time inference. Support structured and unstructured healthcare datasets, distributed systems, microservices, and reliable data infrastructure while collaborating with data science and MLOps teams.

Build and configure a government Databricks workspace, develop ingestion and integration pipelines from enterprise and legacy systems, and prepare data for reporting. Implement Unity Catalog governance, metadata management, auditing, and lineage; configure MLflow workflows; assess security controls for sensitive data; and create architecture documentation, data dictionaries, and SOPs to support long-term sustainment.

Lead the design and optimization of healthcare data pipelines integrating EHR, payer, HIE, and vendor data. Build AWS-based ETL/ELT infrastructure, reusable integration tooling, data quality monitoring, and ML enablement foundations. Ensure HIPAA-compliant handling of clinical data, including security controls, audit logging, encryption, and de-identification. Provide technical leadership, mentor engineers, influence platform architecture, and lead complex partner integrations involving FHIR, HL7v2, CCDA, and healthcare terminology standards.

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.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

Designs, builds, tests, and documents scalable data solutions and pipelines for CMS healthcare fraud, waste, and abuse initiatives. Responsibilities include data warehouse development, CI/CD deployment, orchestration, data quality, governance, AWS-based automation, testing, architecture support, and technical leadership. The role partners with analysts and business stakeholders, leads project initiatives, improves workflow automation, and ensures requirements and milestones are met.

Build PFA’s data organization and own its data platform, architecture, pipelines, integrations, schemas, and data-quality processes. Develop predictive analytics, AI-enabled workflows, reporting layers, dashboards, and executive insights from fragmented healthcare data. Establish engineering standards involving Python, SQL, Git, testing, and maintainable software design. Partner with product, engineering, and business leaders to address data gaps and translate strategic needs into scalable technical solutions.

Build and support scalable data platforms and pipelines, leading migrations such as Snowflake to BigQuery and transitioning reporting to Looker. Responsibilities include data architecture, ETL/ELT, API and marketing integrations, data quality, production troubleshooting, warehousing, performance optimization, and platform modernization. The role partners with analytics and business teams, owns projects through production, documents solutions, and provides technical guidance.

Develop and maintain data engineering solutions using Informatica Data Management Cloud, Control-M, Snowflake, and SQL. Responsibilities include sourcing, transforming, cleansing, ingesting, analyzing, and visualizing data; building automated workflows and scalable ETL processes; supporting data architecture and deployments; and delivering reusable code for large datasets. The role requires collaboration with technical and business partners, troubleshooting, mentoring, and managing multiple priorities.

Design and lead scalable, secure, highly available database infrastructure for multi-terabyte SQL Server environments. Set technical direction for data architecture, performance, real-time pipelines, security, and growth. Troubleshoot complex production issues, optimize queries and indexes, guide cross-functional projects, mentor engineers, review changes, reduce technical debt, and evaluate emerging technologies. Monitor database health using Splunk, Datadog, and Grafana while influencing engineering and Site Reliability strategy.

Develops and maintains data pipelines, datasets, schemas, models, databases, and data flows. Analyzes raw and structured data, improves data quality, builds reporting and data dictionaries, and collaborates with analysts, scientists, and stakeholders. The role supports data security, compliance, visualization, and reporting while modernizing legacy technologies. Requires a flexible schedule supporting 24/7/365 operations and the ability to obtain and maintain a security clearance.