Ardent - Data Engineer
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. • 3 years • 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. • Microsoft Excel • 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. • Experience supporting fraud detection, anomaly detection, financial oversight analytics, or similar analytical environments is preferred. • 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. • Due to the nature of the work we support, all candidates in consideration for this role must be willing to undergo the government issued background investigation process. • Ardent
Responsibilities
• Data Engineering & Pipeline Development • 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. • Data Platform Management • 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. • Data Quality & Governance • 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. • Support data governance initiatives, including documentation, validation, and quality assurance activities. • Collaboration & Analytics Support • 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.
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