Lead Data Engineer

## 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 ## Requirements - 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 DataStage experience including architecture, parallel job design, performance tuning, and enterprise deployment ## Nice to Have ## Benefits