Data Engineer (Public Trust Required)

ICF is seeking a detail-oriented Data Engineer to support the development, optimization, and deployment of large-scale healthcare data pipelines and analytical datasets. This role will focus on cancer registry and population-based data systems. ICF partners with the Centers for Disease Control and Prevention (CDC) to monitor cancer diagnoses, incidence rates, geographic distribution, and treatment patterns. These efforts enable researchers, clinicians, and policymakers to better understand and combat cancer. This position can be based in Rockville, MD or Atlanta, GA and offers a hybrid work arrangement, with 2–3 days per week onsite at the ICF office. Key Responsibilities Design, develop, and maintain scalable, cloud-based data pipelines using Python, PySpark, and SQL in cloud environments. Process and standardize large-scale healthcare datasets, including cancer registry, mortality, and population data. Develop and maintain production databases, analytic datasets, and reporting tables for statistical analysis and surveillance. Perform rigorous data quality validation, reconciliation, and consistency checks across large datasets. Collaborate with epidemiologists, statisticians, and analysts to design and implement data solutions for analytics needs. Troubleshoot data pipeline failures and large-scale data processing issues. Basic Qualifications Minimum of 2 years of experience in data engineering or working with healthcare data environments. BA/BS in computer science, Data Science, Statistics, Public Health Informatics, Bioinformatics, or related field. Strong expertise in data integration, transformation, quality control, and delivery of production-ready data for analytics and reporting platforms. Hands‑on experience with Python, PySpark, Spark SQL, and commonly used Python libraries for data processing and analysis. Experience with Python libraries such as pandas, NumPy, PyArrow, matplotlib, Plotly or similar tools. Experience with cloud platforms such as AWS, Azure, Google Cloud, or similar. Experience handling large databases and high‑volume datasets (millions+ records). Experience with SAS and/or R. Experience implementing data quality validation and QC frameworks. Strong analytical, troubleshooting, and problem‑solving skills. U.S. citizenship or permanent residency required. Ability to obtain and maintain Public Trust suitability for the role. Preferred Skills / Experience Experience with XML, XSLT, or XML path. Proven experience migrating SAS‑based pipelines to Python/PySpark or cloud‑native architectures. Experience with cloud‑based data platforms, data lakes, cloud storage, serverless functions, and containerized applications. Experience developing and optimizing PySpark pipelines for large-scale data processing. Version control experience (e.g., Git, SVN). Team‑player with strong communication skills and willingness to follow processes. Strong analytical and problem‑solving skills. Benefits Pay Range: $98,614.00 – $167,644.00 (based on full‑time employment). We are an equal opportunity employer. #J-18808-Ljbffr ICF