Manager, Data Engineering

JOB SUMMARY

Under the general direction of Central Health's Director of Data Engineering and Integration Services, the Manager of Data Engineering is responsible for leading the design, development, and operational support of Central Health System’s data platforms and data pipelines that power analytics, reporting, data science, AI, and operational insights. This role manages the ingestion, transformation, modeling, and curation of data within the organization’s modern cloud data environment, with Snowflake on Microsoft Azure as Central Health System’s data platform.

The Manager of Data Engineering plays a critical role in Central Health’s transition from a legacy MS SQL Server– based data warehouse environment to a scalable, cloud-native architecture on Azure and Snowflake. This role focuses on analytical data engineering, data platform architecture, and downstream data enablement, and works in close partnership with the Manager of Data Integration, who owns system-to-system interfaces and interoperability.

** This position is considered Hybrid = Individuals in this position may work both at an approved off-site location and onsite at a primary location or multiple locations based on business needs.

Essential Functions

• Lead and manage the Data Engineering team, including hiring, onboarding, prioritization, coaching, and performance management.
• Establish engineering standards, best practices, and development workflows for data pipelines, transformations, and models.
• Partner with the Director of Data Engineering and Integration Services to align roadmaps, capacity planning, and delivery timelines with enterprise priorities.
• Own the design and evolution of Central Health’s analytical data architecture, including Snowflake schemas, data domains, and subject-area models.
• Ensure the Snowflake environment is scalable, performant, secure, and aligned with enterprise architecture standards.
• Guide architectural decisions related to data ingestion, transformation layers, semantic models, and consumption patterns.
• Partner with cloud and infrastructure teams to ensure proper configuration of Azure resources supporting data engineering workloads.
• Oversee the development and maintenance of reliable, automated data pipelines that ingest data from integrationmanaged feeds and other sources into Snowflake.
• Ensure pipelines support batch and incremental processing patterns, data freshness requirements, and downstream service-level expectations.
• Establish standards for pipeline orchestration, monitoring, logging, alerting, and recovery.
• Own production support and incident response for data engineering pipelines and platform components.
• Lead data engineering efforts supporting the migration from legacy MS SQL Server–based data warehouses and marts to Snowflake.
• Design and implement migration strategies, including parallel runs, validation, reconciliation, and phased cutovers.
• Ensure continuity of analytics, reporting, and regulatory deliverables during the migration.
• Modernize data models and transformations as part of the migration, avoiding one-for-one legacy replication where possible.
• Oversee the design and maintenance of curated, analytics-ready data models that support clinical, financial, operational, and population health use cases.
• Partner with Data Governance and Data Quality teams to embed data quality checks, lineage, metadata, and documentation into engineering workflows.
• Ensure data engineering practices support regulatory reporting, auditability, and trusted enterprise metrics.
• Ensure data platforms and pipelines reliably support analytics, BI, data science, and AI use cases.
• Partner with analytics and data science teams to understand data requirements, performance needs, and modeling…