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About the Role

Leads the design, development, and operational support of data platforms and pipelines powering analytics, reporting, data science, AI, and operational insights. Manages data ingestion, transformation, modeling, and curation within a cloud data environment, specifically Snowflake on Microsoft Azure. Plays a key role in transitioning from a legacy MS SQL Server data warehouse to a cloud-native architecture.

Responsibilities

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 integration-managed 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 considerations.
Support feature-ready datasets, historical depth, and refresh cadences required for predictive and generative AI workflows.
Work closely with the Manager of Data Integration to ensure seamless handoffs between system-to-system integrations and analytical data pipelines.
Collaborate with Analytics, Clinical Informatics, Finance, Operations, Security, and Systems Engineering teams to align data engineering deliverables with business needs.
Clearly delineate ownership boundaries, with the Manager of Data Engineering responsible for analytical data platforms and pipelines, and the Manager of Data Integration responsible for interfaces, interoperability, and operational data exchange.
Other duties as assigned.

Requirements

6 years Experience in data engineering, data warehousing, or analytics platform development.
2 years Experience in technical and/or people leadership roles.
2 years Experience supporting or leading migrations from legacy SQL Server environments to modern cloud data platforms.
2 years Experience working in healthcare or other highly regulated environments.
2 years Experience with Snowflake and Microsoft Azure–based data architectures.
2 years Experience supporting BI tools, analytics platforms, or data science workloads.
2 years Exposure to data governance, metadata management, and data quality frameworks.
Familiarity with ELT/ETL orchestration tools and modern data engineering frameworks.

Skills

Snowflake
Microsoft Azure
Data Engineering
Data Warehousing
Analytics Platform Development
Technical Leadership
People Leadership
SQL Server Migration
Cloud Data Platforms
Healthcare Data
BI Tools
Analytics Platforms
Data Science Workloads
Data Governance
Metadata Management
Data Quality Frameworks
ELT/ETL Orchestration Tools
Modern Data Engineering Frameworks

Location

Hybrid

Work Type

Hybrid

Experience Level

6 years Data Engineering
2 years Leadership
2 years Cloud Migration
2 years Healthcare
2 years Snowflake/Azure
2 years BI/Analytics/Data Science Support
2 years Data Governance/Quality Exposure

Education Level

Bachelor's Degree in Computer Science, Information Systems, Health Informatics, Engineering, or a related technical field (or equivalent experience)
Master's or higher Degree in Computer Science, Information Systems, Health Informatics, Healthcare Administration, or a related field (preferred)

About the Company

Central Health System is transitioning from a legacy MS SQL Server–based data warehouse environment to a scalable, cloud-native architecture on Azure and Snowflake.