Lead Data Engineer

Roles and Responsibilities:

• Define the enterprise data engineering architecture and technology standards across DB2, SQL Server, IBM DataStage, IBM Workload Scheduler, Oracle GoldenGate, and AWS
• Lead the multi-year platform modernization roadmap phased migration from legacy on-premises patterns to cloud-native AWS data engineering patterns
• Govern platform health including capacity planning, performance benchmarks, upgrade management, and disaster recovery compliance with BCP/DR standards
• Lead workload rationalization identifying pipelines, stored procedures, and jobs for consolidation, retirement, or re-architecture
• Evaluate and drive adoption of modern data engineering capabilities (Apache Airflow, dbt, AWS Glue, Spark) aligned to Project Catalyst objectives
• Own SLA adherence across all data engineering queues incidents, service requests, small-ticket enhancements, and larger backlog-driven work
• Lead root cause analysis (RCA) for critical data incidents and drive permanent fixes to prevent recurrence
• Lead monthly release cycles including environment coordination, change control governance, and production readiness sign-off
• Maintain full backlog visibility in ServiceNow classification, aging, capacity tracking, and executive-level reporting
• Define and oversee data quality monitoring frameworks, escalation procedures, and continuous improvement programs
• Serve as the primary data engineering relationship owner for senior stakeholders across PHP, PDS/PMG, Quality, and System Services
• Own CSAT measurement and improvement for the data engineering domain, proactively addressing data trust and availability concerns
• Deliver weekly operational and monthly executive reporting on pipeline health, throughput, SLA performance, and platform KPIs
• Develop and own the multi-year data engineering roadmap aligned to Project Catalyst's stabilization-to-modernization progression
• Lead the phased AWS cloud migration strategy for remaining on-premises data engineering components, ensuring continuity and minimal disruption
• Identify and implement automation opportunities to reduce manual pipeline interventions, dataset refreshes, and extract requests
• Lead knowledge management across the engineering team runbooks, architecture diagrams, onboarding playbooks, and continuity documentation

Required Qualifications

• Minimum Degree Required: Bachelors Degree in Engineering, Statistics, Mathematics, Computer Science, Data Science, Economics, or a related quantitative field
• 8+ years of data engineering experience with deep expertise in enterprise ETL/ELT architecture, pipeline design, and large-scale data platform operations
• 3+ years in a formal lead, manager, or technical lead capacity overseeing a data engineering team
• Expert-level SQL proficiency in IBM DB2 and SQL Server including complex schema design, query optimization, and stored procedure management
• Expert-level IBM DataStage experience including architecture, parallel job design, performance tuning, and enterprise deployment
• Deep expertise in IBM Workload Scheduler complex job stream design, dependency management, SLA configuration, and production operations
• Advanced Oracle GoldenGate experience including replication architecture, CDC design, and production support
• Proven AWS data engineering experience in production S3, Glue, RDS, Redshift, Lambda, and IAM-governed data access
• Demonstrated ability to develop and execute multi-year technology roadmaps and lead platform modernization programs
• Experience leading managed services or outsourced delivery models with SLA, CSAT, and throughput accountability

Preferred Qualifications

• Healthcare data engineering experience across claims, clinical (HL7/FHIR), EMR, pharmacy, population health, or regulatory reporting domains
• AWS certification Data Engineer Professional, Solutions Architect Professional, or equivalent
• Experience with modern data stack adoption in enterprise settings Apache Airflow, dbt, Spark, Delta Lake, or equivalent
• Knowledge of HIPAA, HITRUST, CMS, and healthcare data regulatory compliance requirements
• Experience leading on-premises to cloud migrations for large-scale enterprise data platforms
• Familiarity with Tableau, BusinessObjects, or SAS as downstream analytics consumers of engineered data
• Background in agile delivery, DevOps practices, and CI/CD pipelines for data engineering