Data Integration Engineer

McLean, VA, 1099, W2


  • Assemble large, complex sets of data that meet non-functional and functional business requirements
  • Identify, design, and implement internal process improvements including re-designing infrastructure for greater scalability, optimizing data delivery, and automating manual processes
  • Build required infrastructure for optimal extraction, transformation and loading of data from various data sources using AWS and SQL technologies
  • Build analytical tools to utilize the data pipeline, providing actionable insight into key business performance metrics including operational efficiency and customer acquisition
  • Work with stakeholders including data, design, product and executive teams and assisting them with data-related technical issues
  • Establish advanced analysis and data visualization methodologies, models, and tools to derive/predict intelligence outcomes and impacts
  • Implement data validation and cleaning techniques to find and fix or remove inaccurate and irrelevant data
  • Develop and maintain a data warehouse populated with data from numerous sources and with varied formats
  • Build, test, and maintain database pipeline architectures
  • Identify ways to improve data reliability, efficiency, and quality
  • Data pipeline and warehouse performance measurement and optimization
  • Prepare data for predictive and prescriptive modeling in close partnership with in-house data science team
  • Deliver updates to stakeholders based on analytics
  • Ensure compliance with customer’s data governance and security policies
  • Design, develop and maintain scaled, automated, user-friendly systems that will support the needs of the business
  • Be hands-on with ETL to build data pipeline to support automated reporting
  • Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation


  • TS/SCI FSP Clearance
  • 2+ years of relevant work experience in data science or data engineering in big data environments
  • 2+ years of experience in data mining and data-set preparation using SQL
  • 2+ years of experience using data visualization software, such as, Tableau Desktop or Power BI
  • Experience with data modeling, data warehousing, and building ETL pipelines

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