Data Engineer

Long-term contract with a Big 4 consulting firm supporting a hospital and healthcare system engagement Remote: Working MST Hours Travel: Once a month to client site in Albuquerque, NM, 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., * 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.

• 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, * 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