Top Consulting Data Engineering Jobs in Boston, MA

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.

Owns enterprise data architecture across on-premises and cloud systems. Designs data models, integration patterns, governance standards, security controls, and cloud migration strategies. Evaluates data platforms, documents architecture decisions, supports CUI compliance with NIST and CMMC requirements, and partners with technical, security, infrastructure, application, and business teams. Mentors engineers and analysts while communicating technical recommendations to executive and non-technical audiences.

Leads enterprise Microsoft Fabric architecture, strategy, governance, and onboarding standards for large-scale data acquisition and operationalization. Designs Lakehouse, Warehouse, OneLake, semantic, ingestion, and reusable data-product architectures across Medallion layers. Establishes metadata, lineage, observability, security, compliance, and operational controls while advising executives and mentoring architects, engineers, and data modelers. Partners with delivery teams to align platform architecture with analytics, reporting, modeling, and AI initiatives.

Build and maintain scalable data solutions in Palantir Foundry, including ontologies, data objects, pipelines, workflows, analytics, and AI/ML integrations. Translate stakeholder requirements into enterprise data and analytics solutions, manage schemas and governance, troubleshoot platform issues, document processes, and provide user training and support. The role supports FAA data modernization, platform adoption, and continuous improvement of Foundry applications and data models.

Lead managed services for clients' Azure, Databricks, and Microsoft Fabric data environments. Architect, implement, monitor, troubleshoot, and govern data platforms, ETL pipelines, data warehouses, semantic models, reporting, and AI/ML solutions. Manage incidents and SLAs, advise clients on licensing and technical strategy, communicate solutions, document environments, and mentor distributed engineering teams.

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.

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.

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 and maintain research data infrastructure connecting laboratory instruments, analysis pipelines, LIMS, repositories, databases, and cloud platforms. Develop ingestion and data transformation pipelines, manage metadata and persistent identifiers, deploy Kubernetes-based services, maintain CI/CD and infrastructure-as-code, and integrate AI/ML tools for data discovery. The role also evaluates data-management technologies, trains research staff, and produces technical documentation and procedures.

Designs and implements scalable data pipelines, cloud data platforms, storage solutions, and analytics infrastructure. The role focuses on Snowflake and AWS architecture, migrating legacy systems to cloud environments, developing Python and SQL data-processing logic, orchestrating workflows, and implementing Infrastructure-as-Code and CI/CD. The engineer also contributes to data governance, quality, observability, stakeholder communication, mentoring, and technical leadership.