14599 - Data Engineer (Onsite) – Oklahoma City, OK
Start Date: ASAP Type: Temporary Project Estimated Duration: 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.