Senior Manager, Data Engineering

Job Description Summary

The Senior Manager, Data Product Engineering is a hands-on technical leader who leads the design, development, and delivery of data products, pipelines, and analytics solutions that support BCBSA's analytics, reporting, and AI/ML workloads. This role leads a focused engineering team that builds and operates components of the broader data platform — using AWS, Databricks, and Snowflake as the primary stack, alongside modern orchestration, observability, and governance tooling.

This is an execution-focused leadership role. This role is expected to write production code, contribute to data architecture and design decisions, conduct code reviews, troubleshoot complex pipeline issues, and lead production support for their team's workloads — while managing and developing a team of data engineers, coordinating with vendor delivery partners, and applying the engineering standards set by leadership and broader architecture team. This role partners with peers across data engineering, analytics, and product teams to deliver assigned data initiatives on time, with quality, and within established platform patterns.

Job Description

Hands-On Data Product Engineering & Delivery
• Lead a team of data engineers in the design, development, and delivery of scalable data pipelines and data products using AWS, Databricks, Snowflake: Spark, PySpark, Python, and SQL — contributing as a hands-on engineer alongside the team.
• Engage across the complete software development lifecycle for assigned initiatives — requirements analysis, estimation, technical design, development, code review, testing, release planning, deployment, and post-production support.
• Contribute to architecture and design decisions within the scope of owned data products; apply enterprise technical standards, reference architectures, and platform patterns set by Architecture and Leadership.
• Identify opportunities for product modernization, reusability, and engineering improvements, and bring forward recommendations.
• Lead end-2-end engineering delivery for NDW/VBP/CCL data product functions.

ETL/ELT Delivery & Data Pipeline Engineering
• Lead and hands-on contribute to the development of reliable, scalable, and high-performance ETL/ELT pipelines supporting batch, near-real-time, and analytical workloads for assigned data products.
• Build and operate ingestion, transformation, and curation patterns aligned to medallion architecture, lakehouse, and dimensional modeling principles established by the broader data platform.
• Review production code from team members and vendor partners, apply established coding standards, promote reuse of common frameworks, and ensure maintainability, scalability, and reliability of delivered solutions.
• Troubleshoot and resolve pipeline failures, data quality issues, and performance bottlenecks; partner with platform, infrastructure, and cloud engineering teams on complex incidents that span beyond the team's scope.

Cloud Engineering, DevOps & Operational Excellence
• Build solutions on AWS-native services and leverage Databricks and Snowflake as core components for analytics and ML workloads, following established platform architecture patterns.
• Implement and maintain the CI/CD lifecycle for the team's data pipelines: Git-based development, automated testing, deployment automation, infrastructure as code, and rollback patterns — aligned with enterprise DevOps standards.
• Optimize compute, storage, and workload execution across AWS, Databricks, and Snowflake for assigned workloads; apply FinOps practices in day-to-day engineering and surface cost optimization opportunities.
• Implement monitoring, alerting, observability, performance tuning, and production readiness practices for the team's data products in line with platform-wide SLAs and standards.

Product Data Enablement, Quality & Governance
• Deliver data product engineering work that powers BCBSA data products across claims, member, provider, pharmacy, clinical, financial, operational, regulatory, and value-based care domains.
• Bring deep, hands-on expertise across NDW, CCL, and adjacent BCBSA enterprise data assets — applying that knowledge to data model design, source-to-target mapping, lineage, and downstream data product development.
• Partner with product managers, analytics, and data science teams to build curated datasets, semantic models, and reusable data products that support Medicare Advantage, Risk Adjustment, Stars/HEDIS, Cost of Care, and member experience use cases.
• Treat data as a product — applying product thinking to schema design, data contracts, consumer experience, documentation, versioning, and lifecycle management.
• Build data quality, lineage, and metadata capture into pipelines and