Top Data Engineer Jobs in Boston, 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.

Design, build, test, and support scalable cloud data solutions and pipelines. Develop data platforms using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases, and cloud technologies. Establish reusable data engineering patterns, security controls, and operational standards. Collaborate with product managers, engineers, analysts, and data scientists; communicate technical outcomes; mentor data professionals; and deliver resilient, high-performance financial data products.

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.

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

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.

Design, build, test, and maintain cloud-based data pipelines using Azure, Databricks, Cosmos DB, Delta Lake, and Spark. Develop ETL frameworks, data quality validations, data warehousing solutions, and high-throughput libraries. Troubleshoot pipelines and DevOps deployments, analyze semi-structured data, and contribute across the software development lifecycle within an agile team. The role requires onsite work four days weekly and supports financial-industry products.

Own data pipelines into and out of the warehouse, dbt models, data integrity, governance, identity resolution, reverse ETL, and quality monitoring across growth, lifecycle, and brokerage data. Partner with product, marketing, and operations teams to establish data contracts, ensure accurate and timely business metrics, and support regulated financial data requirements.

Build, test, maintain, and improve data pipelines, data models, and ETL/ELT processes supporting firm analytics and reporting. Ensure data accuracy, reliability, governance, monitoring, and observability while troubleshooting quality issues. Collaborate with data engineers, product owners, and business stakeholders; document pipelines and schemas; participate in Agile ceremonies; and use Git for code changes. The role also supports data modernization and may involve cloud data platforms, orchestration tools, and AI/LLM technologies.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Supports the development and maintenance of SQL and Python data pipelines, data models, semantic layers, dashboards, and reports. Performs data wrangling and preprocessing, translates business requirements into data solutions, and contributes to data governance, security, and quality assurance. Collaborates with cross-functional teams supporting marketing, engineering, product development, supply chain, finance, business analytics, and AI initiatives.

Staff Data Engineer responsible for setting technical direction, architecting scalable data platforms, leading cross-functional initiatives, improving reliability and performance, and enabling AI-assisted analytics and development. The role includes hands-on development with Python, SQL, cloud data warehouses, orchestration, infrastructure as code, Kubernetes, Kafka, and streaming pipelines, along with mentoring, technical advocacy, documentation, code reviews, and on-call troubleshooting.

Staff Data Engineer leading architecture and technical direction for real-time and batch motorsports data platforms. Responsibilities include telemetry ingestion, streaming systems, data quality, observability, cloud platforms, reliability, incident response, technical standards, roadmap planning, stakeholder influence, and mentoring. The role delivers scalable solutions supporting race-day operations and cross-functional analytical products.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

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 ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

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.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Build and deliver Azure and Microsoft Fabric data solutions for clients. Responsibilities include developing data pipelines with Azure Databricks, Data Factory, Synapse Analytics, and Fabric; writing SQL, Python, and PySpark; supporting data integration, testing, deployments, troubleshooting, CI/CD, and infrastructure management. The role involves consulting with client stakeholders, collaborating with architects and developers, and delivering modern data platforms and reporting solutions.

Leads enterprise data platform architecture, engineering standards, technical decisions, and development of scalable data products. Designs batch and streaming systems, governance, observability, security, and reliability practices. Partners cross-functionally, mentors engineers, drives modernization and migrations, and establishes reusable patterns for data modeling, orchestration, quality, and platform operations.

Designs and optimizes scalable data infrastructure, including ETL pipelines, data integrations, BI solutions, validation, cleansing, workflow optimization, and troubleshooting. The role supports clinical and operational teams by integrating data from clinical systems, APIs, warehouses, and external sources while ensuring data quality and performance. Collaboration across departments and delivery of data-driven insights are key responsibilities.

Design and maintain Snowflake-based data platforms, cloud-native data lakes, and scalable ETL/ELT pipelines for asset-backed finance analytics. Orchestrate workflows with Dagster and AWS services, ensure data reconciliation and integrity, and support migration from legacy systems. Contribute to project planning, Jira backlog management, testing, cutover planning, and cross-functional go-live readiness.

Design and maintain Snowflake data pipelines and data lake architectures for asset-based finance analytics. Develop ETL/ELT workflows, validate and reconcile financial data, migrate legacy systems, and translate quantitative finance logic into scalable architectures. Support project execution through requirements breakdown, planning, testing, cutover, and go-live readiness. Collaborate across engineering, quantitative analytics, and stakeholder teams.

Design, build, and operate production data pipelines and shared data foundations using SQL, Python, PySpark, Databricks, and cloud technologies. Develop data models, ingestion and transformation workflows, resilient processing, observability, data-quality controls, and secure customer-aware data flows. Collaborate with engineering and product teams, evaluate architecture and tooling, support analytics and application services, contribute to testing and documentation, and mentor other engineers.

Builds and maintains data pipelines and datasets supporting analytics, AI, and modeling. Responsibilities include ingesting healthcare claims and lab data into Snowflake and AWS Iceberg, processing structured and unstructured sources, curating data for AI use cases, implementing medallion architectures, evaluating data quality, and contributing to pilot initiatives using SQL, Python, AWS Glue, Terraform, and Snowflake.

Designs Azure-based data models, simulations, test environments, automated testing frameworks, and performance validation solutions for healthcare data pipelines. Develops test data generators, validates ETL and HL7 FHIR transformations, supports data quality and reliability, and collaborates with engineers and architects on design and code reviews. The role also provides technical guidance on testing architecture, CI/CD, infrastructure as code, resiliency, and large-scale cloud data processing.