Data Engineer

• Build and refine a state-of-the-art cloud data platform powering AI and analytics
• Enable the creation of advanced data products for sophisticated consumers
• Deliver value as part of an agile data engineering team using modern tools and technologies
• Work closely with the data architect and stakeholders
• Help MAPFRE USA transform into a data-driven organization
• Automate infrastructure provisioning
• Integrate security into CI/CD pipelines using DevSecOps practices
• Conduct threat modeling, vulnerability assessments, and incident response
• Monitor and log cloud infrastructure and systems
• Optimize system performance and cloud costs
• Collaborate across Development, Operations, and Security teams
• Support ingestion, transformation, and orchestration of data

Data Platform Engineer II/III

• Support data-pipeline development and migration activities for Legacy Data Warehouse (LDW) to Azure Fabric Lakehouse/Warehouse
• Write efficient SQL queries for data extraction, transformation, and validation across SQL Server 2022, Azure SQL MI, and Synapse
• Develop and debug Python scripts for automation, file ingestion, and data-quality checks
• Assist in building Fabric dataflows, pipelines, and lakehouse tables aligned with enterprise data-modeling standards
• Collaborate with senior engineers on ETL design, optimization, and performance tuning
• Perform unit testing and peer code reviews
• Maintain technical documentation in Confluence/Git
• Monitor data-load processes and assist in resolving job failures, including Zephyr test case execution and JIRA updates
• Learn emerging Azure technologies such as Databricks, Fabric, and Power BI
• Contribute to process-improvement initiatives

Junior Azure Data Engineer

• Define the target data architecture and phased roadmap for operational systems, data lakes, warehouses, integration services, analytics platforms, and customer-facing data products
• Establish domain-oriented data models and product boundaries
• Design logical and physical models, canonical schemas, event structures, data contracts, and master/reference data approaches
• Set standards for data ingestion, transformation, storage, sharing, retention, archiving, and deletion
• Architect AWS data solutions using S3, Lake Formation, Glue, Redshift, Aurora, RDS, DynamoDB, Kinesis, Lambda, Step Functions, SQS, SNS, and API Gateway
• Connect transactional applications with reporting, BI, advanced analytics, machine learning, and external customer use cases
• Define metadata, cataloging, lineage, provenance, quality, observability, semantic consistency, and service-level expectations
• Embed privacy and security controls including IAM, encryption, masking, tokenization, tenant isolation, policy-based access, auditability, retention, and permitted-use controls
• Guide database and workload design for relational, NoSQL, warehouse, and object storage technologies
• Partner with product and business teams to translate information needs into data capabilities
• Evaluate AWS services and data patterns through proofs of concept and evidence-based recommendations
• Provide architecture oversight through production and coach engineers, analysts, and technical leads

Principal Data Architect

• Design, build, test, and support Databricks Lakehouse data pipelines using Spark, Delta Lake, Python, and SQL
• Develop and maintain Databricks notebooks, workflows, jobs, and reusable pipeline components
• Build and maintain curated datasets and analytics-ready models across bronze, silver, and gold Lakehouse layers
• Support data ingestion, migration, and integration between Databricks, On-Premise SQL Server, SaaS platforms, and other enterprise systems
• Partner with analysts, data scientists, business stakeholders, Data Operations, and MDM teams to translate requirements into scalable data solutions
• Monitor, troubleshoot, and optimize Spark jobs and Databricks workflows for performance, reliability, and cost efficiency
• Implement data validation, error handling, data quality checks, security practices, and governance standards
• Maintain, administer, and support the evolution of the Databricks platform
• Support CI/CD and automated deployment practices using Azure DevOps and Databricks Asset Bundles

BI Data Engineer II

• Build advanced technology solutions and lead data engineering activities on mission-driven projects
• Guide development and deployment of pipelines and platforms that organize disparate data
• Mentor a multi-disciplinary team of analysts, data engineers, developers, and data consumers
• Conduct analytical exploration and data examination
• Oversee assessment, design, building, and maintenance of scalable client platforms

• Serve as the product-specific client-facing expert for Verily’s Precision Health Platform, Pre, including delivering product demos, showcasing the solution at industry events, and supporting customers through pilots.
• Spearhead strategic sales initiatives that drive revenue growth, from initial lead generation to successful deal closure.
• Craft compelling value propositions, uncover and address customer needs, negotiate favorable terms, and secure contracts.
• Collaborate closely with product and marketing teams by sharing customer and partner feedback on pricing, market challenges, innovation, and the competitive landscape.
• Partner closely with Account Executives and cross-functionally across Verily to achieve individual and shared goals, contributing to team success and company-wide milestones/OKRs.
• Lead the sales, solutioning, and service process with new and existing life sciences customers, including matching solutions to customer needs, driving pricing and contracting, and ensuring implementation readiness.

Lead Solution Platform, Data and AI

• Design, build, and optimize data pipelines and ETL workflows
• Develop scalable data models, data structures, and data integrations
• Work with Azure Synapse, Data Factory, Data Lake, and Databricks
• Ensure data quality, governance, security, and compliance
• Partner with business stakeholders, analysts, and data scientists
• Contribute to or lead complex, cross-functional data initiatives
• Improve platform reliability through monitoring, validation, and automation
• Create documentation, runbooks, and data lineage
• Mentor junior engineers in senior-level roles
• Build foundational skills and work alongside experienced engineers at associate level
• Own pipelines, models, and datasets and drive quality, governance, and performance improvements at Data Engineer II level
• Lead complex data initiatives and architectural decisions, design enterprise-scale Azure solutions, establish best practices, and drive reliability, scalability, and cost efficiency at senior level

• Own end-to-end configuration control for CAD models, BOMs, and technical documentation within the PLM system
• Ensure revision management, baselining, effectivity, lifecycle states, and release readiness across engineering artifacts
• Maintain alignment between CAD structures, EBOMs, MBOMs, and released documentation
• Serve as the authoritative source for released product data, ensuring integrity, traceability, and audit readiness
• Own and enforce ECR/ECN workflows and ensure changes are reviewed, approved, and traceable
• Manage as-designed versus as-built alignment across product variants and lifecycle phases
• Coordinate engineering release activities across mechanical, electrical, manufacturing, quality, and documentation teams
• Drive resolution of configuration, release, and data quality issues
• Lead release readiness reviews, change control boards, and cross-functional working sessions
• Facilitate prioritization, action-item tracking, and issue resolution across engineering teams
• Identify and remove blockers, manage dependencies, and drive accountability without direct supervisory authority
• Apply Agile, Scrum, Kanban, or similar methodologies to improve workflow efficiency and predictability
• Define and enforce naming conventions, metadata standards, classification structures, and release criteria
• Resolve configuration discrepancies, duplicate records, and data integrity issues
• Improve PLM, configuration management, and release processes
• Develop and maintain configuration management standards, best practices, and governance procedures
• Ensure PLM data structures support customer audits, compliance requirements, and quality management systems
• Maintain traceable records linking requirements, designs, changes, configurations, and released products
• Support customer, regulatory, and internal audits through configuration history, impact analysis, and documentation retrieval

Lead PLM, Configuration Engineer – Data Center Infrastructure

• Shape Trinity’s data infrastructure and enable data-driven growth and innovation
• Manage a team of skilled data engineers
• Ensure availability, reliability, and scalability of data systems
• Develop and implement data quality standards, policies, and procedures
• Design, develop, and maintain data quality control processes and workflows
• Perform data profiling and analysis to identify data quality issues and root causes
• Lead data cleansing and enrichment, including deduplication, standardization, and augmentation
• Develop and execute data validation tests and checks
• Maintain documentation of data quality processes, procedures, and issue resolutions
• Collaborate with cross-functional teams to define data quality requirements
• Provide data quality training and awareness programs
• Implement monitoring and reporting systems for data quality metrics and improvements
• Present data quality findings to senior management
• Continuously improve data quality processes and strategies
• Design and develop databases
• Perform data analysis for client project support
• Design, develop, and implement ETL processes
• Contribute to continual improvement of best practices
• Collaborate with internal team members to meet client project needs
• Explore industry data sources, analytical packages, database technologies, and trends

Director, Data Engineering

• Define the strategy, architecture, governance, operating model, and decision rights for Biogen’s clinical and operational data ecosystem
• Integrate stewardship, standards, SDTM, data engineering, transparency operations, imaging, reusable data products, and AI-ready platform capabilities into one accountable organization
• Partner across Development, QSDO, R&D IT, Legal, Privacy, Regulatory, Clinical Data Management, Biostatistics, Medical Writing, Biomarkers, Medical, and external collaborators
• Own the enterprise Data Stewardship and AI-ready data engineering framework, including metadata, lineage, data quality, access, FAIR-aligned practices, reusable data products, and Snowflake/Cortex AI-enabled capabilities
• Lead modern data engineering practices, SDTM, clinical data standards, reusable clinical data products, automation, digital protocol initiatives, submission-ready pipelines, and AI/ML-ready architecture
• Provide strategic leadership for Clinical Trial Transparency, controlled data sharing, participant-centric data access, consent/privacy alignment, and global regulatory expectations
• Define strategic roadmaps, technical architecture priorities, governance models, investment areas, and decision rights
• Build a high-performing organization of approximately 24 professionals across stewardship, standards, SDTM, CTT, imaging, data engineering, and AI/ML enablement

Data Engineer jobs in Massachusetts

Data Platform Engineer II/III

Junior Azure Data Engineer

Principal Data Architect

BI Data Engineer II

Data Engineer

Lead Solution Platform, Data and AI

Data Engineer

Lead PLM, Configuration Engineer – Data Center Infrastructure

Director, Data Engineering

Senior Director, Head of Data Stewardship, Data Engineering