Lead Data Engineer
Position Summary<\/span>
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 \u2014 from\n team management and platform governance to enterprise architecture, executive stakeholder engagement, and multi\-year modernization strategy. As a key leader in Project Catalyst, the Lead Data Engineer is accountable for the reliability, performance, and evolution\n of PHS's 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\n function at the leadership level.<\/span>
Key Responsibilities<\/span>
Define the enterprise data engineering architecture and technology standards across DB2, SQL Server, IBM DataStage, IBM Workload Scheduler, Oracle GoldenGate, and AWS<\/span>
Lead the multi\-year platform modernization roadmap \u2014 phased migration from legacy on\-premises patterns to cloud\-native AWS data engineering patterns<\/span>
Govern platform health including capacity planning, performance benchmarks, upgrade management, and disaster recovery compliance with PHS BCP/DR standards<\/span>
Lead workload rationalization \u2014 identifying pipelines, stored procedures, and jobs for consolidation, retirement, or re\-architecture<\/span>
Evaluate and drive adoption of modern data engineering capabilities (Apache Airflow, dbt, AWS Glue, Spark) aligned to Project Catalyst objectives<\/span>
Own SLA adherence across all data engineering queues \u2014 incidents, service requests, small\-ticket enhancements, and larger backlog\-driven work<\/span>
Lead root cause analysis (RCA) for critical data incidents and drive permanent fixes to prevent recurrence<\/span>
Lead monthly release cycles including environment coordination, change control governance, and production readiness sign\-off<\/span>
Maintain full backlog visibility in ServiceNow \u2014 classification, aging, capacity tracking, and executive\-level reporting<\/span>
Define and oversee data quality monitoring frameworks, escalation procedures, and continuous improvement programs<\/span>
Serve as the primary data engineering relationship owner for senior stakeholders across PHP, PDS/PMG, Quality, and System Services<\/span>
Own CSAT measurement and improvement for the data engineering domain, proactively addressing data trust and availability concerns<\/span>
Deliver weekly operational and monthly executive reporting on pipeline health, throughput, SLA performance, and platform KPIs<\/span>
Develop and own the multi\-year data engineering roadmap aligned to Project Catalyst's stabilization\-to\-modernization progression<\/span>
Lead the phased AWS cloud migration strategy for remaining on\-premises data engineering components, ensuring continuity and minimal disruption<\/span>
Identify and implement automation opportunities to reduce manual pipeline interventions, dataset refreshes, and extract requests<\/span>
Lead knowledge management across the engineering team \u2014 runbooks, architecture diagrams, onboarding playbooks, and continuity documentation<\/span>
Required Qualifications<\/span>
Minimum Degree Required: Bachelor\u2019s Degree in Engineering, Statistics, Mathematics, Computer Science, Data Science, Economics, or a related quantitative field<\/span>
8+ years of data engineering experience with deep expertise in enterprise ETL/ELT architecture, pipeline design, and large\-scale data platform operations<\/span>
3+ years in a formal lead, manager, or technical lead capacity overseeing a data engineering team<\/span>
Expert\-level SQL proficiency in IBM DB2 and SQL Server including complex schema design, query optimization, and stored procedure management<\/span>
Expert\-level IBM DataStage experience including architecture, parallel job design, performance tuning, and enterprise deployment<\/span>
Deep expertise in IBM Workload Scheduler \u2014 complex job stream design, dependency management, SLA configuration, and production operations<\/span>
Advanced Oracle GoldenGate experience including replication architecture, CDC design, and production support<\/span>
Proven AWS data engineering experience in production \u2014 S3, Glue, RDS, Redshift, Lambda, and IAM\-governed data access<\/span>
Demonstrated ability to develop and execute multi\-year technology roadmaps and lead platform modernization programs<\/span>
Experience leading managed services or outsourced delivery models with SLA, CSAT, and throughput accountability<\/span>
Preferred Qualifications<\/span>
Healthcare data engineering experience across claims, clinical (HL7/FHIR), EMR, pharmacy, population health, or regulatory reporting domains<\/span>
AWS certification \u2014 Data Engineer Professional, Solutions Architect Professional, or equivalent<\/span>
Experience with modern data stack adoption in enterprise settings \u2014 Apache Airflow, dbt, Spark, Delta Lake, or equivalent<\/span>
Knowledge of HIPAA, HITRUST, CMS, and healthcare data regulatory compliance requirements<\/span>
Experience leading on\-premises to cloud migrations for large\-scale enterprise data platforms<\/span>
Familiarity with Tableau, BusinessObjects, or SAS as downstream analytics consumers of engineered data<\/span>
Background in agile delivery, DevOps practices, and CI/CD pipelines for data engineering<\/span><\/span>
<\/div><\/span>
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 \u2014 from\n team management and platform governance to enterprise architecture, executive stakeholder engagement, and multi\-year modernization strategy. As a key leader in Project Catalyst, the Lead Data Engineer is accountable for the reliability, performance, and evolution\n of PHS's 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\n function at the leadership level.<\/span>
Key Responsibilities<\/span>
Define the enterprise data engineering architecture and technology standards across DB2, SQL Server, IBM DataStage, IBM Workload Scheduler, Oracle GoldenGate, and AWS<\/span>
Lead the multi\-year platform modernization roadmap \u2014 phased migration from legacy on\-premises patterns to cloud\-native AWS data engineering patterns<\/span>
Govern platform health including capacity planning, performance benchmarks, upgrade management, and disaster recovery compliance with PHS BCP/DR standards<\/span>
Lead workload rationalization \u2014 identifying pipelines, stored procedures, and jobs for consolidation, retirement, or re\-architecture<\/span>
Evaluate and drive adoption of modern data engineering capabilities (Apache Airflow, dbt, AWS Glue, Spark) aligned to Project Catalyst objectives<\/span>
Own SLA adherence across all data engineering queues \u2014 incidents, service requests, small\-ticket enhancements, and larger backlog\-driven work<\/span>
Lead root cause analysis (RCA) for critical data incidents and drive permanent fixes to prevent recurrence<\/span>
Lead monthly release cycles including environment coordination, change control governance, and production readiness sign\-off<\/span>
Maintain full backlog visibility in ServiceNow \u2014 classification, aging, capacity tracking, and executive\-level reporting<\/span>
Define and oversee data quality monitoring frameworks, escalation procedures, and continuous improvement programs<\/span>
Serve as the primary data engineering relationship owner for senior stakeholders across PHP, PDS/PMG, Quality, and System Services<\/span>
Own CSAT measurement and improvement for the data engineering domain, proactively addressing data trust and availability concerns<\/span>
Deliver weekly operational and monthly executive reporting on pipeline health, throughput, SLA performance, and platform KPIs<\/span>
Develop and own the multi\-year data engineering roadmap aligned to Project Catalyst's stabilization\-to\-modernization progression<\/span>
Lead the phased AWS cloud migration strategy for remaining on\-premises data engineering components, ensuring continuity and minimal disruption<\/span>
Identify and implement automation opportunities to reduce manual pipeline interventions, dataset refreshes, and extract requests<\/span>
Lead knowledge management across the engineering team \u2014 runbooks, architecture diagrams, onboarding playbooks, and continuity documentation<\/span>
Required Qualifications<\/span>
Minimum Degree Required: Bachelor\u2019s Degree in Engineering, Statistics, Mathematics, Computer Science, Data Science, Economics, or a related quantitative field<\/span>
8+ years of data engineering experience with deep expertise in enterprise ETL/ELT architecture, pipeline design, and large\-scale data platform operations<\/span>
3+ years in a formal lead, manager, or technical lead capacity overseeing a data engineering team<\/span>
Expert\-level SQL proficiency in IBM DB2 and SQL Server including complex schema design, query optimization, and stored procedure management<\/span>
Expert\-level IBM DataStage experience including architecture, parallel job design, performance tuning, and enterprise deployment<\/span>
Deep expertise in IBM Workload Scheduler \u2014 complex job stream design, dependency management, SLA configuration, and production operations<\/span>
Advanced Oracle GoldenGate experience including replication architecture, CDC design, and production support<\/span>
Proven AWS data engineering experience in production \u2014 S3, Glue, RDS, Redshift, Lambda, and IAM\-governed data access<\/span>
Demonstrated ability to develop and execute multi\-year technology roadmaps and lead platform modernization programs<\/span>
Experience leading managed services or outsourced delivery models with SLA, CSAT, and throughput accountability<\/span>
Preferred Qualifications<\/span>
Healthcare data engineering experience across claims, clinical (HL7/FHIR), EMR, pharmacy, population health, or regulatory reporting domains<\/span>
AWS certification \u2014 Data Engineer Professional, Solutions Architect Professional, or equivalent<\/span>
Experience with modern data stack adoption in enterprise settings \u2014 Apache Airflow, dbt, Spark, Delta Lake, or equivalent<\/span>
Knowledge of HIPAA, HITRUST, CMS, and healthcare data regulatory compliance requirements<\/span>
Experience leading on\-premises to cloud migrations for large\-scale enterprise data platforms<\/span>
Familiarity with Tableau, BusinessObjects, or SAS as downstream analytics consumers of engineered data<\/span>
Background in agile delivery, DevOps practices, and CI/CD pipelines for data engineering<\/span><\/span>
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