Data Engineer

Data Engineer/Remote

Must have
Datastage,
Expert level SQL
ETL

Preferred:
Healthcare

Position Summary

• 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.