AWS Principal Data Engineer

AWS Principal Data Engineer Boston Scientific was recognized by Forbes as one of the Best Workplaces for Engineers in 2026, reflecting a culture where engineers do meaningful work. The AWS Principal Data Engineer is a senior, hands-on technical leader and principal individual contributor responsible for designing, building and evolving scalable, reusable AWS data platform solutions. In this role, you will transform governed data into trusted, production-ready data products that power clinical insights, commercial analytics and AI-driven innovation. Operating at the intersection of architecture, engineering execution and governance automation, you will shape how data flows from source systems through ingestion, transformation, quality enforcement and semantic enrichment. You will partner closely with data engineering, platform engineering, security, site reliability engineering (SRE), DevOps and governance teams to deliver a modern data platform that operates as a product rather than a collection of siloed solutions. As a principal-level individual contributor, you will establish technical direction, define reusable patterns and influence engineering standards across teams through expertise, architecture leadership and mentorship. Your responsibilities will include: Design reusable AWS data platform architectures and lead the implementation and migration of complex data pipelines supporting governed ingestion, batch, streaming, change data capture (CDC), event-driven processing and data product delivery, ensuring validated cutovers and stage-gated reconciliation. Translate governance requirements into automated platform capabilities, including data contracts, schema validation, data quality controls, lineage capture, metadata management and access enforcement. Build and maintain infrastructure-as-code modules, CI/CD templates and Terraform-based provisioning frameworks that enable scalable, secure and governed platform deployment. Architect and implement platform capabilities for security, observability, resiliency and cost management, including encryption, identity and access management (IAM), telemetry, service-level objectives (SLOs) and cost attribution. Design governed lakehouse and data product patterns using AWS-native services and open table formats, including Apache Iceberg, to support scalable analytics, AI workloads and enterprise data products. Evaluate emerging technologies, document architectural decisions and establish engineering standards that advance platform maturity, scalability and operational excellence. Provide technical leadership through architecture reviews, reference designs and mentoring, enabling engineering teams to adopt best practices and deliver predictable, high-quality solutions. Required qualifications: Bachelor's degree in computer science, data engineering, information systems or a related technical field. Minimum of 13 years' experience in data engineering, including a minimum of 6 years' experience designing and delivering cloud-native data platforms in AWS. Minimum of 4 years' experience building scalable data platform solutions, including infrastructure-as-code, CI/CD, data integration (batch, streaming and CDC) and governance automation such as data contracts, quality controls, lineage and access management. Demonstrated expertise with AWS data services, including S3, Glue, Athena, Lambda, MSK, Kinesis, DynamoDB, RDS, Redshift, EMR, Step Functions, DMS, ECS, Lake Formation and DataZone. Production-level experience with Snowflake. Strong programming and platform engineering expertise using Python, Terraform, Bash and distributed data processing frameworks such as Spark and Flink. Experience with advanced data architecture and modeling concepts, including knowledge graphs, ontologies, semantic layers and GenAI-enabled data solutions. Demonstrated ability to serve as a principal-level technical leader by establishing standards, influencing architectural direction and driving adoption across multiple engineering teams. Ability to travel up to 10%. Preferred qualifications: Master's degree in computer science, data engineering or a related technical field. Knowledge of modern lakehouse and data governance technologies, including Alation, Atlan, DataHub, Informatica or Databricks. Experience supporting AI/ML platforms, including governed analytical workspaces and model lifecycle management. Familiarity with regulated industries such as medical devices, healthcare or life sciences.