Data Engineer I

Knowesis, Inc. is seeking a Data Engineer to support the Pandemic Response Accountability Committee (PRAC). The role designs and maintains data pipelines and Lakehouse environments that enable analytics, fraud detection, and investigative work, while ensuring reliable and well-governed data flows.

Responsibilities:

• Design, build, and maintain scalable ETL pipelines to ingest, transform, and prepare structured and unstructured data from diverse sources (flat files, JSON, XML, Excel, APIs, graph databases)
• Develop and optimize data ingestion workflows supporting fraud analytics, anomaly detection, and investigative use cases
• Implement robust data quality control processes, ensuring accuracy, completeness, lineage, reliability, and performance across all analytic datasets
• Support data scientists and analysts by preparing analytic-ready datasets for machine learning, graph analytics, and fraud detection models
• Maintain and enhance data pipelines and transformation logic to ensure continuity of service and no degradation in analytic output quality
• Prepare and deliver high-quality datasets used for investigative analysis, link analysis, and case development
• Support investigative teams by rapidly ingesting new data sources and preparing structured datasets for fraud pattern identification
• Ensure data used in investigative products meets PRAC standards for reliability, traceability, and evidentiary integrity
• Collaborate with OIG and law enforcement partners to support data needs for investigative leads, referral packages, and case summaries
• Evaluate emerging data engineering tools, ingestion frameworks, and Lakehouse technologies; provide recommendations aligned with PRAC mission needs
• Collaborate with PRAC’s OCIO, OCISO, and OCDO to configure and optimize data environments, including Databricks Unity Catalog and SQL Server managed instances
• Support ingestion and integration of new data sources, including schema design, transformation logic, and quality control
• Assist PRAC in analyzing new data engineering technologies and providing documentation to support Government procurement decisions
• Maintain awareness of evolving fraud schemes and data engineering best practices to enhance PRAC’s analytic capabilities

Requirements:

• Minimum of three (3) years of professional experience in data engineering or a closely related field
• Demonstrated ability to design, build, and maintain scalable ETL pipelines across diverse data sources
• Strong proficiency in SQL and Python, or equivalent technologies, with experience ingesting and transforming data from flat files, JSON, XML, Excel, APIs, and graph databases
• Experience loading, managing, and optimizing data within platforms such as Databricks Unity Catalog and SQL Server managed instances, including work with streaming and batch ingestion frameworks and modern Lakehouse architecture
• Proven capability implementing data quality control processes to ensure data quality, lineage, reliability, and performance
• Familiarity with data governance, data quality, and data management practices consistent with enterprise data management (EDM) standards
• Experience supporting fraud detection, anomaly detection, or financial analytics environments
• Bachelor's degree (BA/BS) in Computer Science, Data Engineering, Information Systems, or a related technical field
• Public Trust (HSPD-12)
• Experience supporting analytic environments within federal oversight, OIG, or law enforcement contexts
• Experience integrating multi-agency or multiprogram datasets into scalable pipelines
• Experience with cloud-based analytic platforms (Azure Databricks, AWS Glue, etc.)
• Experience collaborating with data scientists, investigators, and analysts in cross-functional teams
• Master's degree (MS/MA) in Data Engineering, Data Science, Information Systems, or a related discipline