Lead Data Engineer

Role & responsibilities Lead Data Engineer - Analytics Organization (AO) | Presbyterian Healthcare Services Location: Bangalore/Hyderabad Experience: 8-12 Years Data Engineering | 3+ Years Lead or Manager Capacity Environment: AWS Cloud | DB2 / SQL Server | IBM DataStage | IBM Workload Scheduler | Oracle GoldenGate Position Summary The Lead Data Engineer is the most senior technical and people leader within the data engineering practice at Presbyterian Healthcare Services' Analytics Organization. This role owns the full data engineering function from team management and platform governance to enterprise architecture, executive stakeholder engagement, and multi-year modernization strategy. Key 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 PHS 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. Own CSAT measurement and improvement for the data engineering domain. 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. 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 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. 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. 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, BusinessObjects, or SAS as downstream analytics consumers of engineered data. Background in agile delivery, DevOps practices, and CI/CD pipelines for data engineering. Note:- Share relevant profiles to sruthi.b@sageit.in Role: Big Data Engineer Industry Type: IT Services & Consulting Department: Engineering - Software & QA Employment Type: Full Time, Permanent Role Category: Software Development Education UG: Any Graduate