Top Data Engineer Jobs in Boston, MA

Build and architect scalable cloud data platforms, pipelines, and applications using technologies such as Python, Spark, Databricks, and Snowflake. Lead large-scale data initiatives, make architectural decisions, establish engineering patterns, and ensure platform resilience and operational efficiency. Collaborate with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical concepts to stakeholders. Evaluate data platform choices and deliver solutions supporting machine learning, distributed systems, and evolving enterprise data needs.

Lead large-scale data engineering initiatives, designing scalable cloud data platforms, pipelines, and architectures. Use Python, Spark, SQL, Databricks, Snowflake, AWS, relational and NoSQL databases to deliver resilient data solutions. Mentor engineers, analysts, and data scientists; establish engineering standards; evaluate platform choices; and collaborate with product and software teams to support enterprise data transformation and AI capabilities.

Designs reusable data ingestion and transformation components, automates data quality, governance, testing, lineage, and deployment workflows, and improves pipeline observability and reliability. The role supports monitoring, incident response, cloud and container integrations, infrastructure as code, documentation, reusable datasets, and Agile platform improvements. Candidates need five or more years of data engineering experience plus expertise in orchestration, distributed processing, storage, scripting, automation, and version control.

Implements data pipelines and platform features, automates validations and operational tasks, supports CI/CD and infrastructure-as-code changes, monitors platform reliability, assists with migrations and performance tuning, and maintains technical documentation. The role also contributes to data quality, incident response, cloud configuration, and incremental platform improvements. This is a hybrid position with potential urgent IT incident support responsibilities.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

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, 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.

Build and optimize scalable data pipelines and cloud infrastructure using Azure, Snowflake, dbt, Python, and orchestration tools. Integrate diverse data sources, develop automated ETL/ELT workflows, implement CI/CD and testing, and support data governance, security, and documentation. Collaborate with analytics engineers, data scientists, researchers, and stakeholders to create data models supporting analytics, machine learning, visualization, and cancer research initiatives.

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.

Lead the design and evolution of BJ’s Databricks Lakehouse, building batch and real-time pipelines, integrating digital and operational data, and establishing data quality, governance, CI/CD, and cost optimization. Develop AI-powered analytics and Databricks agents, own the digital data roadmap, and mentor a distributed engineering team while partnering with product and business leaders.

Build and scale programmable data infrastructure supporting Vesalius Therapeutics’ human cellular biology platform. The role contributes to large-scale genetics, genomics, stem cell, cellular phenotyping, and artificial intelligence initiatives designed to uncover causal biology and identify therapeutic intervention points.

Build and operate laboratory data infrastructure supporting Benchling, sample tracking, APIs, web applications, instrument integrations, and analysis-ready data. Develop integrations across automation platforms, instruments, LIMS/ELNs, imaging systems, databases, and downstream pipelines. Create scientist-facing tools, investigate data discrepancies, and apply robust software engineering practices. Partner across scientific, automation, IT, and data teams while independently owning laboratory systems and planning for future platform needs.

Designs, builds, and optimizes enterprise Databricks Lakehouse pipelines and data products using Spark, Delta Lake, SQL, and Python. Leads complex data initiatives, production support, governance, CI/CD, workflow orchestration, monitoring, and modernization of legacy SQL Server solutions. Partners with stakeholders, optimizes performance and cost, mentors engineers, and drives engineering standards across Azure data platforms.

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.

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.

Designs, builds, operates, and modernizes secure ETL/ELT data pipelines for public health interoperability. Responsibilities include using NiFi, Kafka, Python, and SQL; deploying pipelines on Azure or AWS; implementing CI/CD with GitHub Actions; monitoring with Grafana; troubleshooting production systems; supporting legacy migrations; and ensuring data security, compliance, scalability, and reliability. The role collaborates with architects, integration engineers, data scientists, DevOps/SRE, and product teams.

The AVP, Data Engineer will design and build data ingestion, transformation, and replication pipelines using SQL, Python, dbt, and Dagster. Responsibilities include translating requirements into technical designs, developing data products, debugging production issues, supporting testing, and improving data engineering practices. The role also involves data modeling, mentoring junior engineers, conducting code reviews, and providing technical leadership while remaining hands-on. Experience with modern data platforms, workflow orchestration, cloud ecosystems, and insurance or financial services is preferred.

Designs, develops, optimizes, and maintains scalable data workflows and pipelines using Matillion, HVR, and Databricks. Performs data transformation, modeling, SQL development, analytics, and troubleshooting while ensuring data quality, security, governance, and compliance. Collaborates with data scientists, analysts, architects, and business data owners to translate requirements into technical solutions and documents data processes. The role requires substantial data engineering experience, U.S. citizenship, and an active DoD Secret clearance.

Leads data architecture and engineering modernization across business domains. Designs and implements complex pipelines, integrations, data models, and orchestration using Snowflake, AWS, and Airflow. Establishes data quality, governance, security, testing, observability, CI/CD, and reliability practices. Provides architectural guidance, evaluates technical solutions, leads cross-team initiatives, mentors engineers, and improves platform performance and cost efficiency.

Build and maintain production ETL pipelines for structured and unstructured data, integrate machine learning models for enrichment, develop data transformations and quality monitoring, design warehouses and dimensional models, establish governance frameworks, and contribute to data architecture and platform improvements. Collaborate with data scientists, analysts, and engineering teams to deliver reliable, actionable intelligence.

Leads the architecture, development, and production operation of complex data pipelines and platforms. Resolves scalability, data quality, reliability, and architectural challenges; evaluates data technologies; mentors engineers; establishes engineering standards; partners with product, analytics, and operations teams; communicates technical trade-offs; and conducts proofs of concept to guide platform strategy.

Develop and maintain scalable data stores, ETL/ELT workflows, data pipelines, and platforms for structured and unstructured data. Build software to retrieve, parse, process, and convert data for reporting and analytics. Collaborate with analysts, developers, and data consumers in agile environments while supporting platform scaling, monitoring, and operations. The role requires a Secret clearance and may involve cloud, distributed computing, streaming, NoSQL, and data warehousing technologies.

Build and maintain scalable Snowflake-based ELT/ETL pipelines, production data models, and observability frameworks. Translate business requirements into reliable solutions supporting analytics, BI, and ML. Establish data quality, governance, lineage, security, and compliance practices while optimizing warehouse performance, cost, and data freshness. Collaborate with Analytics, Product, and Engineering teams, contribute to architecture decisions, evaluate Snowflake tooling, and use AI-assisted development tools to accelerate delivery.

Designs, builds, and optimizes scalable data collection, storage, access, and analytics systems. Creates and automates reliable data pipelines, supports data quality and governance, ensures SLA and performance compliance, and monitors pipeline operations. The role requires collaboration with analytics, business, and customer teams, with potential work involving AI/ML pipelines, Databricks, DevOps practices, streaming data, and event-driven architectures.