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Job Description & How to Apply Below
Roles and Responsibilities:
Define the enterprise data engineering architecture and technology standards across DB2, SQL Server, IBM Data Stage, IBM Workload Scheduler, Oracle Golden Gate, 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 Service Now — 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:
Bachelor’s 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 Data Stage 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 Golden Gate 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, Business Objects, or SAS as downstream analytics consumers of engineered data
Background in agile delivery, Dev Ops practices, and CI/CD pipelines for data engineering
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