Data Engineer (onsite)

14599 - Data Engineer (Onsite) -

Oklahoma City, OK Start Date:

ASAP Type:

Temporary Project Estimated Duration:

6-12 months with possible extensions

Work Setting:

100% of the time at the Client's site. No telecommuting or remote work. This is a non-negotiable requirement from the client. Only candidates able to relocate as required should apply to avoid removal from future consideration.

Required:

Availability to work 100% of the time at the Client's site in Oklahoma City, OK (required); Hands-on Data Engineering experience (5+ years); Experience with SQL including correlated subqueries and window functions; Experience with cloud platforms (GCP Big Query) including query performance optimization with partitioning strategies; Experience with Python for data pipeline development, automation, and API integration including pandas, NumPy, and SQL Alchemy; Experience with ETL transformation tools such as Azure Data Factory, GCP Dataproc, Dataflow, SSIS, and dbt; Experience with orchestration tools; Experience with REST APIs for data ingestion and system interoperability; Experience with version control (Git); Experience with R for statistical analysis and data manipulation; Experience with Python ML Libraries.

Preferred:

Experience in a HIPAA-regulated environment with data privacy and security requirements; Experience with standards-based health data exchange (HL7 v2/v3, FHIR); Experience with cell suppression and statistical disclosure logic within SQL for public-facing health data outputs; Experience with SAS; Bachelor's degree in Computer Science, Engineering, Data Engineering, or related field. Responsibilities include but are not limited to the following: Design, implement, and maintain ELT/ETL pipelines across cloud platforms (Azure, GCP, AWS); Architect and modernize data acquisition and ingestion pipelines for large-scale healthcare data; Implement and manage data storage solutions (data lakes, warehouses) utilizing appropriate partitioning, security, and lifecycle policies; Plan and execute data migrations across platforms including schema mapping, data validation, and cutover coordination; Design and architect schemas to support migration of transactional database structures to data warehouse environments including dimensional modeling; Evaluate and integrate new and emerging data sources, link datasets across systems, and develop processes to support novel data types; Document data architectures, lineage, and standards and provide technical guidance and mentorship.