Data and Integration Job Engineer

Description

The Data and Analytics Engineer designs, builds, secures, and maintains CORE's enterprise analytics data platform. This hands-on position establishes a scalable and governed data warehouse using dimensional modeling, including fact and dimension tables; automates data acquisition and integration through ETL or ELT processes; and delivers reliable, reusable data structures for reporting, advanced analytics, and future artificial intelligence capabilities. 

Working under the supervision of the Chief Information and Supply Chain Officer, the engineer supports CORE's donor management system and integration of clinical, operational, financial, workforce, and other enterprise data. The position partners closely with the Chief Performance Officer, Performance Team, Data Analyst, system owners, vendors, and business and clinical subject matter experts to translate approved requirements into sustainable technical solutions. The engineer is responsible for platform reliability, technical data quality, security, performance, monitoring, documentation, and controlled change management. 

Essential Job Functions:

• Data warehouse architecture and dimensional modeling -Design, build, and maintain scalable analytics data structures, including fact and dimension tables, conformed dimensions, defined grain, keys, historical tracking, schemas, views, stored procedures, and reusable transformation layers. Establish architecture standards that support enterprise reporting and analysis.
• Data integration automation and migration support - Develop, test, deploy, monitor, and maintain ETL or ELT pipelines that integrate data from the donor management system and other clinical, operational, financial, workforce, and enterprise systems. Use appropriate database connections, APIs, secure file transfers, incremental loads, and migration processes. Support source-to-target mapping, historical conversion, reconciliation, and cutover activities.
• Platform operations security and reliability - Manage the technical operation of the analytics platform, including role-based access, secrets and connection management, environment separation, deployment controls, performance tuning, capacity monitoring, backup and recovery coordination, and incident troubleshooting. Maintain appropriate development, testing, and production practices.
• Data quality testing metadata and lineage - Implement automated and repeatable controls for completeness, validity, timeliness, duplicates, referential integrity, and reconciliation. Maintain technical metadata, data lineage, source-to-target mappings, transformation logic, data dictionaries, runbooks, and pipeline documentation. Coordinate exceptions with the Data Analyst, Performance Team, and data owners.
• Analytics semantic models and AI readiness - Develop and maintain curated datasets and governed semantic models that support Power BI, advanced analytics, and approved AI use cases. Organize data so it is reusable, well-described, appropriately secured, and suitable for controlled analytical and AI experimentation. Support technical prototypes when requested.
• Requirements collaboration governance and professional development - Translate approved requirements into technical designs and estimates; participate in data governance, project, vendor, and departmental meetings; communicate risks and dependencies; complete assigned action items; and maintain relevant knowledge of data engineering, cloud platforms, security, analytics, and responsible AI practices.

• Promotes the Mission, Vision, and Core Values of CORE.
• Maintains confidentiality of all employee and donor/recipient information.
• Perform other duties and special projects as assigned.

Non-Essential Job Functions:

• Participate in committees, workgroups, and special organizational initiatives as assigned.
• Provide backup support for approved analytics-platform operations, data integrations, and technical documentation. 
• Represent CORE in external professional associations, conferences, or user groups when assigned.

 

Qualifications

Required Education and Experience:

• Bachelor's degree in computer science, information systems, data engineering, software engineering, data science, analytics, or a related field, or equivalent education and directly relevant experience. 
• Five or more years of experience in data engineering, data warehousing, analytics engineering, business intelligence engineering, or database development, including hands-on responsibility for production data solutions. 
• Advanced SQL skills, including database design, complex queries, stored procedures, query optimization, troubleshooting, and performance tuning in SQL Server or a comparable relational database platform. 
• Demonstrated experience with dimensional data modeling, including fact and dimension tables, grain, surrogate keys, conformed dimensions, historical tracking, and star-schema design. 
• Experience designing and operating ETL or ELT pipelines across databases, APIs, secure files, and other enterprise sources, including incremental processing, error handling, monitoring, and recovery. 
• Experience with source control, structured testing, release management, operational monitoring, and separation of development, testing, and production environments. 
• Experience implementing data-quality controls, reconciliation, metadata, lineage, and operational documentation. 
• Knowledge of data security, role-based access, encryption, credential management, and privacy requirements for confidential information. 
• Strong analytical, problem-solving, organizational, documentation, and communication skills. 

Preferred Education and Experience:

• Experience with SQL Server, SSIS, Azure Data Factory, Microsoft Fabric, Azure Synapse, Power BI semantic models, SSRS, or comparable cloud and hybrid data platforms. 
• Experience in healthcare, organ procurement, transplantation, clinical operations, or another regulated and mission-critical environment.
• Experience with Python, PowerShell, PySpark, APIs, and healthcare interoperability formats or standards such as HL7 or FHIR. 
• Experience preparing governed data for forecasting, predictive analytics, machine learning, natural-language interfaces, or other responsible AI use cases. 
• Experience with data governance, master and reference data, data catalogs, infrastructure as code, CI and CD practices, or relevant technical certifications.