Top Data Engineer Jobs in Boston, MA

Build and maintain scalable batch and streaming data pipelines, analytics warehouses, transformation layers, and dimensional data models. Perform SQL-based analysis, support dashboards and reporting, monitor data quality and delivery SLAs, troubleshoot incidents, and improve operational reliability. Collaborate with engineers, analysts, data scientists, and business stakeholders while contributing to documentation, governance, observability, and automation.

Build scalable data ingestion, transformation, and loading infrastructure using Spark, SQL, AWS, and Databricks. Develop entity resolution systems, CI/CD pipelines, anomaly detection, and data quality processes. Design scalable processing systems, orchestration workflows, storage patterns, and data warehouse solutions. Collaborate remotely, document technical designs, scope projects, and solve complex data engineering and data science problems.

Build and scale distributed data pipelines and architectures supporting AI products and research. Responsibilities include processing large datasets with Spark, designing AWS data systems, managing SQL and NoSQL storage, writing Python and SQL, ensuring data quality and reliability, and implementing automation, orchestration, monitoring, and scalable data operations.

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 reliable data pipelines supporting money movement, fraud response, and client reporting. Integrate internal and external APIs, orchestrate workflows with Airflow on Kubernetes, and implement testing, monitoring, alerting, and observability. Debug production issues, document root causes, improve pipeline efficiency, collaborate with stakeholders, and contribute to technical designs. The role requires Python, SQL, Airflow, Snowflake, dbt, and GCP experience, with opportunities for increased end-to-end ownership.

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).

Build scalable cloud-native data and machine learning solutions using Databricks, Spark, PySpark, and SQL. Develop batch and real-time healthcare data pipelines, enterprise data models, Lakehouse architectures, semantic layers, and AI-assisted analytics workflows. Partner directly with business and clinical stakeholders to discover requirements, prototype solutions, deliver demos, drive adoption, and provide technical leadership. Ensure HIPAA-compliant security, governance, lineage, auditability, and regulatory readiness.

Owns the architecture and implementation of Absentia Labs’ biomedical data platform. Designs schema-driven models, scalable batch and streaming pipelines, and cloud-native infrastructure supporting machine learning workflows. Establishes data quality, validation, provenance, lineage, and versioning practices while partnering with scientists, ML engineers, and product leads. Provides architectural leadership, mentorship, and guidance on performance, cost, scalability, and correctness.

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.

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.

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.

Lead the design and implementation of a scalable contract data model, including migration strategies, data architecture, pipelines, warehouses, analytics layers, governance, security, and quality monitoring. Partner with Product, Engineering, Data Science, ML, Professional Services, and customer-facing teams. Drive architecture decisions, provide technical mentorship, lead reviews, and manage multi-quarter data initiatives for a SaaS platform.

Develop and maintain data delivery processes, workflows, data quality solutions, and visualizations supporting consumer lending. Analyze complex datasets, identify root causes, resolve production issues, and provide actionable recommendations. Collaborate with technical and business stakeholders, document methodologies, and communicate findings effectively. The role requires SQL, Python, Tableau, cloud data environment, database, and large-scale dataset experience.

Build and maintain connectors for Oracle B2C Service, SharePoint, OneDrive, Teams, and Sitecore. Develop an AWS ingestion pipeline that extracts, transforms, deduplicates, enriches, permission-tags, indexes, and retires content in S3 and Amazon Bedrock Knowledge Bases. Implement synchronization, throttling, retries, failure isolation, monitoring, inventory reporting, documentation, and client transition support.

Lead customer-facing data engineering engagements from architecture through production handover. Build and optimize batch and streaming Lakehouse pipelines on Databricks using PySpark, SQL, and Delta Lake. Establish Medallion architecture conventions, implement data quality and automated testing, optimize Spark performance and platform costs, and mentor engineers. Partner with architects and explain technical trade-offs to customers.

Designs and executes Salesforce data migrations, including extracting, transforming, cleansing, mapping, loading, validating, and reconciling large datasets. Develops ETL workflows and Salesforce API integrations, supports testing and deployment, troubleshoots data issues, maintains performance and integrity, and documents migration processes. Collaborates with architects, developers, and stakeholders to deliver reliable data solutions.

Lead the architecture and development of UKG’s Unified FinOps Data Platform. Build scalable cloud-native data products and pipelines for cloud billing, usage, cost allocation, forecasting, optimization, reporting, and unit economics across AWS and GCP. Establish governance, data quality, lineage, observability, CI/CD, and infrastructure-as-code standards. Support AI-enabled forecasting and optimization, collaborate with Finance and Engineering stakeholders, mentor engineers, and drive adoption of self-service reporting.

Own the design and operation of Navanta’s data backbone, including secure bank-system ingestion, lakehouse architecture, semantic data modeling, reconciliation, quality, observability, and governance. Partner with AI/ML, security, and platform teams to deliver governed, queryable data products. Lead data contracts, lineage, PII classification, access controls, pipeline reliability, and onboarding of new bank environments.

Build, test, deploy, and operate data pipelines and transformations for financial and economic datasets. Monitor production workflows, implement alerting, logging, automated testing, and data-quality controls, and perform root-cause analysis. Collaborate with engineering, DevOps, application, investment, and research teams to deliver documented solutions. Analyze investment data, support model research, and identify automation opportunities while developing expertise in private markets data.

Develop enterprise data warehouse solutions and ETL pipelines using Azure Data Factory, Databricks, Snowflake, and related technologies. Process structured and unstructured healthcare data, including HL7 and FHIR files. Build stored procedures, perform data warehouse modeling, prepare ETL specifications, and use Python, source control, and scripting tools to support data integration and engineering initiatives.

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.

Designs, builds, and operates scalable batch and streaming data pipelines, ETL/ELT workflows, cloud solutions, and secure Python REST APIs. Develops data foundations for machine learning, generative AI, embeddings, and retrieval-based applications. Ensures data quality, security, observability, lineage, and reliability while optimizing performance and cost. Leads architecture and code reviews, establishes engineering standards, mentors engineers, and collaborates with product, analytics, data science, and machine learning teams.

Develops, architects, and maintains production ETL/ELT and C#/.NET data pipelines across cloud data platforms. Responsibilities include supporting Iceberg, DynamoDB, AWS Lambda, and Trino/Starburst systems; onboarding data sources; tuning performance; monitoring data quality; resolving incidents; coordinating schemas; documenting processes; mentoring engineers; and supporting releases, deployments, architecture, and roadmap decisions.

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.