Top Data Engineer Jobs in Boston, MA

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 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.

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.

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.

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.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

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).

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

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.

Contributes to building and optimizing a large-scale data lake and reliable data pipelines for cybersecurity analytics. Responsibilities include data modeling, distributed processing, workload optimization, monitoring, observability, CI/CD, code reviews, and supporting MLOps. The role collaborates with senior engineers to improve processes and deliver cost-effective cloud data solutions.

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.

Design and build scalable data infrastructure, including lakehouse architecture, ClickHouse deployments, ETL/ELT pipelines, and data models connecting Postgres with analytics systems. Establish data quality, observability, performance, cost, and reliability standards. Collaborate with product and engineering teams, define data contracts and specifications, support real-time analytics, and use AI-assisted coding and LLMs to accelerate pipeline development and transformation.

Supports ETL pipeline implementation, data ingestion, data quality validation, schema and RBAC configuration, database performance testing, interoperability testing, and production migration activities. Maintains metadata registries, documents ingestion defects and remediation recommendations, validates post-migration data, and maintains database scripts and pipeline code in version control. The position is fully remote and supports a federal data migration program.

Develop and deploy Databricks Delta Live Tables ETL pipelines in a cloud lakehouse environment. Aggregate, normalize, transform, and validate geospatial data from authoritative sources. Document ingestion results, data quality defects, remediation actions, and test outcomes. Maintain version-controlled code with peer reviews, support GIS-based spatial validation, and assist with interoperability testing. The role supports federal endangered-species data integration and requires proficiency in Python or Scala, SQL, data quality validation, schema documentation, and geospatial data concepts.

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.

Design, build, and maintain scalable batch and streaming data pipelines, cloud infrastructure, and the Enterprise Data Hub. Partner with stakeholders to productionize ingestions, ensure data quality, implement metadata/catalog solutions, mentor junior engineers, and support analytics and ML workloads using cloud and big-data tooling.

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.

Data Engineer II will modernize BlueLabs’ data platform by leading Kimball dimensional modeling and legacy warehouse migrations, building reliable production data pipelines, and enforcing data quality standards. The role requires advanced SQL, Python, and dbt expertise, hands-on Snowflake and Redshift experience, and ownership of testing, documentation, observability, and scalable architecture. The engineer will collaborate cross-functionally, guide Data Engineer I team members, contribute to architectural decisions, and translate business logic into maintainable, cloud-agnostic data layers.

Designs, builds, and optimizes scalable data pipelines, ETL workflows, integrations, and reporting solutions. Develops SQL, Python, and Spark data-processing solutions using Azure Data Factory and Microsoft Fabric, while monitoring production reliability and performance. Collaborates with stakeholders, participates in code reviews, documents technical solutions, mentors junior engineers, and improves engineering practices. Requires strong data modeling, cloud data platform, Git, communication, and problem-solving skills.

Design, develop, and maintain data pipelines, ETL processes, integrations, data infrastructure, and complex data models. Build and support data warehouses and analytics solutions using SQL, PL/SQL, Oracle, Snowflake, and cloud technologies. Collaborate with analysts, architects, developers, data scientists, and business stakeholders throughout the delivery lifecycle. Provide production support, troubleshoot issues, tune database performance, and contribute to data-driven reporting and AI tools.