Data Engineer

Ardent is a company focused on supporting the federal government's national security and defense priorities. They are seeking a Data Engineer to design, develop, and maintain data engineering solutions that enable advanced analytics and data-driven decision-making.

Responsibilities:

• Design, develop, and maintain scalable ETL/ELT pipelines to support enterprise data integration and analytics
• Ingest, transform, and integrate data from diverse sources, including flat files, JSON, XML, Excel, REST APIs, graph databases, and other structured and unstructured data formats
• Develop and optimize SQL and Python-based data processing solutions to support efficient data ingestion and transformation
• Build and maintain reusable, scalable data workflows that support business intelligence, reporting, and advanced analytics
• Load, manage, and optimize data within modern data platforms, including Databricks Unity Catalog and SQL Server Managed Instances
• Support both batch and streaming data ingestion frameworks
• Implement and maintain modern Lakehouse architecture solutions to improve scalability, performance, and accessibility
• Monitor and optimize database and pipeline performance to ensure efficient processing and storage
• Implement data quality controls to ensure the accuracy, consistency, reliability, and integrity of enterprise data
• Maintain data lineage and metadata to support governance and regulatory compliance
• Apply enterprise data management (EDM) standards and best practices throughout the data lifecycle
• Support data governance initiatives, including documentation, validation, and quality assurance activities
• Collaborate with cross-functional teams, including data analysts, software developers, architects, and business stakeholders, to understand data requirements and deliver effective solutions
• Support analytical environments focused on fraud detection, anomaly detection, financial oversight, and other data-driven initiatives
• Troubleshoot and resolve data pipeline, integration, and performance issues while continuously improving existing processes

Requirements:

• Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related technical field (or equivalent combination of education and experience)
• Minimum of 3 years of professional experience in data engineering or a related field
• Demonstrated experience designing, building, and maintaining scalable ETL/ELT pipelines across multiple data sources
• Strong proficiency in SQL and Python or equivalent technologies used for data engineering and transformation
• Experience ingesting and transforming data from a variety of formats, including: Flat files, JSON, XML, Microsoft Excel, REST APIs, Graph databases, Additional structured and unstructured data sources
• Experience working with Databricks Unity Catalog, SQL Server Managed Instances, or comparable enterprise data platforms
• Experience with streaming and batch ingestion frameworks and modern Lakehouse architecture
• Strong understanding of data quality, data lineage, performance optimization, and enterprise data management principles
• Familiarity with data governance, data quality, and data management practices aligned with Enterprise Data Management (EDM) standards
• Excellent analytical, problem-solving, and communication skills with the ability to collaborate effectively across technical and business teams
• Due to the nature of the work we support, all candidates selected for this position must be willing to undergo a U.S. Government background investigation
• Experience supporting fraud detection, anomaly detection, financial oversight analytics, or similar analytical environments