Top Data Engineer Jobs in Boston, MA

Develop and maintain data pipelines, infrastructure, and integrations supporting analytics, data science, and business intelligence. Ensure data quality and integrity, optimize workflows, and collaborate with Operations, IT, Accounting, HR, Property Services, Marketing, and other departments. The role requires SQL Server, programming, shell scripting, cloud, containerization, version control, and data pipeline knowledge, with occasional travel and flexible scheduling as project deadlines require.

Leads the design, development, and optimization of scalable data pipelines and analytics platforms. Builds ETL/ELT workflows using Databricks and Spark, integrates and transforms source data into curated models, manages SQL Server data, and maintains documentation and lineage. Collaborates with product, analytics, and engineering teams to deliver reliable data solutions. Applies responsible AI practices and delivers projects independently within established timelines.

Build and maintain batch data pipelines and ETL/ELT workflows using SQL, Python, APIs, databases, and files. Develop datasets, data models, quality checks, monitoring, documentation, tests, and deployment processes. Troubleshoot pipeline issues, support releases and backfills, modernize legacy processes toward secure cloud-based solutions, and provide business intelligence insights while collaborating with analysts, software engineers, and business partners.

Designs, develops, and operates secure, scalable data pipelines, platforms, integrations, and data models. Responsibilities include ETL/ELT development, data quality, observability, troubleshooting, architecture, cloud modernization, performance optimization, production support, and collaboration with technical and business stakeholders. The role also mentors engineers, establishes engineering standards, and supports long-term data strategy.

Design, build, test, deploy, and support governed data products and pipelines for supply chain operations. Develop Snowflake and dbt models, optimize workloads, implement data quality and observability controls, and use Databricks and Spark for scalable analytics and AI-ready data foundations. Partner with product, engineering, analytics, and business teams to define requirements and data contracts, lead initiatives through production, document solutions, and improve reliability, security, and engineering standards.

Build and maintain large-scale data pipelines and analytics platforms for EDA datacenter observability, telemetry, reliability analysis, and capacity forecasting. Develop batch and streaming workflows, data models, quality frameworks, monitoring, and retention strategies using metrics, logs, traces, and hardware health data. Partner with observability engineers, data scientists, analysts, and infrastructure teams to deliver scalable, reliable, discoverable datasets for analytical and predictive use cases.

Lead data engineering for client reporting, measurement, internal operations, and AI-driven initiatives. Build and maintain Snowflake pipelines and dbt models, integrate marketing and business data, troubleshoot issues, and maintain governance, privacy, and consent controls. Develop semantic layers and governed access for AI tools. Shape architecture and technical roadmaps, translate business needs into data solutions, and build and lead a data support team.

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 manage healthcare data and machine learning pipelines using Python and SQL. Responsibilities include feature engineering, data modeling, schema design, integrating new data sources, documenting and versioning code, scaling pipelines, managing client data SLAs, and collaborating with data scientists, project managers, clients, and healthcare stakeholders.

Design, build, and optimize scalable data pipelines, warehouses, lakes, and processing systems for analytics and business intelligence. Ensure data quality, security, reliability, and cost efficiency while collaborating with data scientists, analysts, and engineers. Maintain technical documentation, support Databricks solutions, and apply emerging data engineering technologies. The role requires strong programming, SQL, ETL, orchestration, data modeling, cloud, and big data expertise.

Architects and leads enterprise data engineering platforms, including scalable ETL/ELT pipelines, streaming frameworks, cloud infrastructure, data warehouses, and AI/ML data systems. Establishes engineering standards, governance, monitoring, data quality, security, and schema management practices. Designs AI agents using LLMs, partners across business and infrastructure teams, and makes high-impact architectural decisions for reliable, secure, multi-region data environments.

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.

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.

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, builds, tests, and maintains scalable data infrastructure and ETL/ELT pipelines for mission partners. Uses AWS Glue, Glue Catalog, and Step Functions to integrate, migrate, transform, and validate data. Develops data models, schemas, queries, and production-ready datasets while collaborating with analytics and business teams. The role may also support AI/ML pipelines, data governance, DevOps workflows, Databricks, and real-time streaming architectures.

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.

Lead pre-sales technical engagements to design and optimize scalable cloud lakehouse data platforms (Snowflake/Databricks). Provide architecture reviews, ETL/ELT pipeline design, governance and AI-ready data foundations, support services sales, and translate technical tradeoffs into actionable recommendations for engineering and executive stakeholders.

Designs, builds, and maintains Databricks data pipelines and analytics solutions using SQL and Python. Collaborates with development and IT teams, applies DevOps and CI/CD practices, supports Power BI dashboards, and contributes to data modeling, management, and governance. The role requires extensive Databricks and cloud-platform experience, strong communication skills, fluent English, and at least five years of relevant experience.

Build scalable data ingestion, transformation, and loading infrastructure using Spark, SQL, AWS, and Databricks. Develop entity resolution systems, CI/CD pipelines, anomaly detection, and high-quality data products. Design scalable processing and storage patterns, orchestration workflows, and data warehousing solutions while collaborating remotely and documenting technical designs.

Build and maintain Snowflake and dbt data models using medallion architecture, CDC ingestion, star schemas, and slowly changing dimensions. Develop governed semantic datasets for BI and AI workloads, implement testing, documentation, monitoring, alerting, row-level security, and performance tuning. Partner with product and business stakeholders to translate reporting needs into reliable multi-tenant SaaS data solutions.

Build and maintain scalable, fault-tolerant data pipelines, warehouses, and analytics solutions. Transform complex structured and semi-structured data, automate delivery processes, improve data quality and reliability, and support enterprise data strategies. Collaborate with analysts, data scientists, actuaries, and business teams to translate requirements into solutions. Research new technologies, troubleshoot issues, maintain version-controlled code, and mentor data engineering team members.

Designs, develops, integrates, and supports modern data solutions for healthcare claims, revenue, Medicare, and payer data. The role includes on-premises data warehousing, cloud migrations, SQL and Python/PySpark development, dimensional modeling, troubleshooting, performance optimization, and process improvement. The engineer collaborates with engineering, analytics, data science, and product teams in an agile environment.

Designs and maintains scalable data models, semantic layers, ETL/ELT workflows, data-quality frameworks, and governed analytics products for commercial operations. Owns data solutions from requirements through deployment, monitoring, documentation, and support. Partners with technical and business stakeholders to deliver trusted reporting, dashboards, scorecards, productivity measurements, and AI-enabled decision support.

Own end-to-end client data migrations: reverse-engineer legacy ETL, design and build Airflow DAGs and Spark jobs, validate and reconcile data at scale, document architecture and decisions, and coordinate cross-functional teams through go-live and hypercare.