Data Engineer

JOB DESCRIPTION The Data Engineer is responsible for the development, maintenance, and operational support of enterprise data pipelines, ETL processes, and data platform components within the Data & Analytics Managed Services. The Data Engineer is accountable for the reliability, performance, and evolution of enterprise data pipelines, ensuring the organization transitions from foundational stabilization toward a modern, cloud-native data platform. This individual sets the technical direction, drives delivery excellence, and represents the data engineering function at the leadership level. Working across a complex environment of stored procedures, scheduled and streaming jobs, and a recently AWS-migrated data platform, this role ensures reliable, high-quality data flows that power enterprise reporting, analytics, and decision-making across clinical, operational, financial, and health plan domains. Key Responsibilities • Develop, maintain, and optimize SQL-based ETL processes, stored procedures, and data transformations across DB2, SQL Server, Datastage, Collibra and AWS • Define the enterprise data engineering architecture and technology standards across DB2, SQL Server, IBM DataStage, IBM Workload Scheduler, Oracle GoldenGate, Collibra and AWS • Develop and maintain data integration workflows from source systems to analytics platforms, including validation and reconciliation logic • Build and maintain data pipelines using IBM DataStage and UNIX scripting for enterprise data integration workflows • 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 • Monitor pipeline health proactively, detect anomalies, and resolve data quality and availability issues within defined SLAs • Support Dev/QA/Prod environment management including release coordination and production readiness validation • Assist with AWS stabilization activities for analytics data layers post migration from on-premises infrastructure • Track and manage all work through ServiceNow, ensuring accurate classification, status updates, and SLA compliance • Collaborate with Tableau, SAS and BusinessObjects developers to ensure data availability and pipeline reliability for reporting • Participate in L1/L2 triage for pipeline incidents, data quality failures, and integration issues • Contribute to runbook documentation and standard operating procedures for supported pipelines and jobs • Collaborate with cross-functional teams, including data engineers, data scientists, and business analysts, to deliver end-to-end solutions across client domains • Own SLA and KPI 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 • 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 • 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 • 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 • Oversee end-to-end delivery of managed data analytics services to clients, ensuring projects meet business requirements, timelines, and quality standards • Manage client escalations and ensure timely resolution of issues. REQUIRED SKILLS AND EXPERIENCE Bachelors Degree 8-12 yrs experience ETL production support IBM DataStage w/ DB2 SQL Server and stored procedures AWS preferred L1/L2/L3 support Customer facing On call rotation (1 week at a time) After hours on call Working with an offshore team • 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 • 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 NICE TO HAVE SKILLS AND EXPERIENCE • 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