Lead Data Warehouse Engineer Artificial Intelligence-Ready Mount Sinai ()

The Scientific Computing and Data team at the Icahn School of Medicine at Mount Sinai partners with scientists and clinicians to accelerate scientific discovery.The ()platformcontainspatientdata generated fromthe clinical careprocesses atthe is a cloud-basedhigh-performanceSAP HANAdata platformwith electronic health record (EHR) data in an OMOP data format. It also contains metadata from and links to raw data sets in other modalities such as radiology genomics and pathology. Researchers can access the data throughdirectdatabaseaccess AIagents cohort query tools and the Minerva high-performance ingests OHDSIs Observational Medical Outcomes Partnership (OMOP)-formatted EHR data from the Mount Sinai Data Warehouse. is integrated with Minerva ahigh-performance computerwith>21 petaflops of raw computational power and the raw radiology genomics and pathology data sets. Anexpert team of 20 PhD/MDcomputational scientists biomedical informaticists and computerscientistspartner with researchers and clinicians toeffectively and efficientlyutilizethese resourcesfor translational science.

The Lead Data Warehouse Engineer is a senior technical specialist responsible for leading the ongoing integration of multi-modal clinical data into the data warehouse. The Lead Data Warehouse Engineer will assess the state of data integration identify opportunities for data integration based on researchers priorities develop a plan to integrate metadata and data and execute on the data integration. The incumbent will work collaboratively with other members of the data platform and Mount Sinai Data Warehouse teams to lead technical efforts for the integration of multi-modal data sets resulting in expanded functionality. The data warehouse is built on the SAP HANA technology stack and MSDW is built on MySQL.

• Design databases and pipelines that balance functionality performance cost and development time; evaluate technical options with the product manager.
• Design build test and maintain data pipelines that extract/capture data from source systems transform and augment those data and integrate it into a multi-modal data repository.
• Serve as a team leader; contribute to project planning work breakdown dependency sequencing and release management.
• Develop and promote standards conventions design patterns DevOps/SDLC best practices and operational procedures for pipelines and warehouse maintenance.
• Mentor junior engineers in data warehousing data engineering skills and operational support.
• Design build and maintain data management processes including loading flat files (csv tsv pipe-delimited JSON).
• Lead design sessions code walkthroughs peer reviews and produce technical documentation.
• Tune database objects stored procedures and pipelines to optimize performance and minimize compute and storage costs.
• Monitor database and pipeline operations; lead troubleshooting and remediation of failures; provide occasional after-hours on-call support.
• Collaborate with DBAs and system administrators on backups performance tuning statistics/index maintenance and patching.
• Provide high-quality customer service to researchers clinicians and internal partners; maintain a sciencedriven customer-focused approach.
• Ensure patient privacy and data security in compliance with IRB & cybersecurity policies HIPAA 42 CFR Part 2 NYS Article 27-F and other regulations.
• Stay current with emerging technologies to improve capabilities efficiency quality or cost.
• Identify improvements in procedures technology compliance and data privacy/security.
• Periodically assist DBAs with user provisioning backups restorations capacity planning and performance monitoring.
• Perform related duties as assigned.

• Bachelors degree in a technical discipline; Masters degree preferred
• 12-15 years preferred of related experience including 8 years of experience designing developing and maintaining relational databases data pipelines and dimensional/OLAP warehouses.

Preferred:

• Expert knowledge of data warehousing: 3NF & dimensional modeling (fact table types SCDs) change data capture incremental loads data lineage source-to-target mappings pattern-based & parameter-driven development.
• Expert-level experience with data engineering technologies: SQL indexing stored procedures UDFs sequences dynamic SQL data transformation tools job orchestration tools for data processing.
• Experience with DevOps/SDLC best practices; Agile (Scrum Kanban) with JIRA and Confluence; version control with git.
• Strong communication and customer service skills for working with researchers clinicians administrators and IT staff.
• Excellent critical thinking problem-solving multitasking and collaboration skills; ability to work independently in a fast-paced environment.
• Preferred experience with healthcare data (EHR billing/claims cost accounting) Epic Clarity/Caboodle data models (OMOP i2b2 PCORnet).
• Experience with database administration: configuration performance tuning partitioning materialized views permissions backups & restorations.