Top Data Engineer Jobs in Boston, MA

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

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, implement, and test cloud-based ETL/data pipelines and datasets to support analytics and data science workloads. Collaborate with data scientists and analysts, translate business requirements into technical solutions, and follow data engineering best practices.

The Data Engineer will develop data pipelines, support automation, and collaborate within an Agile team to manage complex data sets, ensuring performance and reliability in data processing.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Lead architecture and design of the Beacon Datastore: define scalable data models, build event-driven pipelines, implement features, draft RFCs, coach engineers, and align platform standards to support analytics and clinical workflows.

Designs and optimizes ETL pipelines supporting analytics, data science, and machine learning. Builds labeling, retraining, MLOps, model monitoring, and production inference workflows. Contributes to lakehouse architecture, data modeling, governance, lineage, and quality. Uses infrastructure as code and containerization to create reproducible environments, collaborates with data scientists and BI teams, participates in code reviews, and improves engineering practices.

Design, build, maintain, and optimize Spark and Scala ETL/ELT pipelines on AWS for terabyte- and petabyte-scale datasets. Work across S3, Snowflake, Kafka, and monitoring systems, resolve production data-quality and performance issues, write automated tests, participate in architecture and code reviews, and support on-call operations. Use generative AI to improve pipeline development, monitoring, and engineering productivity.

Designs and maintains healthcare data pipelines, ETL workflows, databases, schemas, and analytical frameworks. Analyzes claims and related healthcare data to identify trends and insights, while ensuring data quality and integrity. Creates reports, dashboards, and visualizations for nontechnical stakeholders. Collaborates with healthcare policy experts, analysts, and stakeholders to translate requirements into data solutions and improve infrastructure, processes, and analytics capabilities.

Build and scale the data backbone for HR, payroll, finance, and insurance products. Design data models across warehouses and data lakes, develop and optimize batch and real-time ETL/ELT pipelines, improve data quality and governance, tune SQL queries, address data integration gaps, and collaborate with engineering, BI, analytics, and business stakeholders.

Design, build, and maintain large-scale ETL/ELT data pipelines and cloud data architectures (AWS/Azure). Optimize Spark/Databricks/Synapse jobs, orchestrate workflows with Airflow, manage SQL/NoSQL databases, implement governance/security, create dashboards, integrate APIs, support ML model pipelines, and deploy using Docker/DevOps practices while monitoring pipeline health.

Builds and maintains scalable data pipelines and cloud infrastructure supporting an AI biomedical research platform. Responsibilities include ingesting, harmonizing, validating, and documenting multimodal neurodegeneration datasets; managing cloud storage, compute, permissions, and costs; implementing de-identification and data governance workflows; supporting research data repositories and AI/ML teams; and maintaining data lineage, catalogs, and technical documentation. The role requires strong Python and SQL skills, software engineering discipline, cloud experience, and neurodegeneration research data expertise.

Design, build, and maintain ETL pipelines, databases, and data models to process healthcare claims and related data. Ensure data quality, analyze claims data for trends, and produce reports and visualizations for nontechnical stakeholders. Collaborate with policy and analytics teams to translate requirements into data solutions and improve data infrastructure and analytics capabilities.

Designs, develops, optimizes, and maintains enterprise data platforms, databases, ETL workflows, reporting solutions, and business intelligence systems. Leads technical design, SDLC processes, production troubleshooting, documentation, stakeholder collaboration, and automation efforts. Requires advanced Microsoft SQL Server, T-SQL, and SSIS expertise, plus experience with data warehousing, BI, dashboards, and Microsoft DevOps. Cloud data services, APIs, Microsoft BI tools, and Python or Java are preferred or advantageous.

Designs, develops, and optimizes enterprise data platforms, databases, ETL workflows, data integrations, reporting, and business intelligence solutions. Leads technical designs, automated process improvements, production troubleshooting, documentation, and dashboard development. Collaborates with stakeholders and vendors, supports scalability and cloud migration, and participates in an off-hours support rotation. Requires advanced SQL Server, SSIS, T-SQL, SDLC, and data engineering expertise.

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, develops, and maintains scalable data pipelines and workflows using Matillion, HVR, and Databricks. Performs data modeling, SQL-based transformation, ETL/ELT optimization, and data quality management. Collaborates with data scientists, analysts, architects, and business data owners to translate requirements into secure, governed data solutions. Documents processes and ensures compliance with security and regulatory standards, including U.S. government cloud requirements.

Participate in an internship focused on database modeling and optimization, data analysis and consolidation, data quality, and data support. Work alongside experienced professionals on real-world projects while developing technical and professional skills. The program requires prior knowledge of data concepts, spreadsheets, databases, programming logic, and analytical problem-solving.

Build and maintain scalable batch and real-time data pipelines, model data to power analytics and ML, and operate distributed data systems. Collaborate with business, engineering, and data science teams to ensure data quality, documentation, and debugging while adopting new technologies and working in an ambiguous, fast-changing environment.

Build and maintain low-latency, highly available distributed data systems and services to deliver high-quality data for product, analytics, and ML. Partner with data scientists and product teams to design scalable, observable, multi-tenant data products using batch and real-time processing frameworks. Ensure 24x7 reliability, monitoring, and operational excellence while owning end-to-end delivery.

Design, build, and own large-scale batch and streaming data pipelines and core Ads data products. Collaborate with analytics, data science, and engineering teams to model, transform, govern, and deliver high-quality ad datasets for analysis, ML, and reporting while ensuring privacy compliance.

Build scalable, privacy-focused data frameworks and ETL pipelines using big-data technologies (Spark, Presto, Flink, Kafka, Hive). Ensure appropriate handling of personal data, support privacy and legal needs, partner cross-functionally, and lead system design while mentoring teammates.

Build and maintain scalable data pipelines and integrations using Databricks and Azure. Transform and model large structured and unstructured datasets to support reporting, analytics, and operational needs. Implement data quality, monitoring, and governance; troubleshoot pipeline performance; collaborate with stakeholders and mentor junior engineers.

Lead and mentor a data engineering team to design, build, test, and support cloud-based, real-time data platforms and distributed microservices using modern big data, streaming, and data warehousing technologies to deliver scalable customer and enterprise experiences.

Lead Data Engineer responsible for designing data models, implementing data solutions, managing data pipelines, and collaborating with teams to drive technical initiatives.