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Data Management – Data Analyst – Sr

McLean, Virginia, United States · Contract · On site

Job Title: Senior Data Analyst

Location: Richmond, VA or McLean, VA (Hybrid)

Duration: 15 Months


Job/Role Description:

  • This role supports enterprise data management strategy by developing and improving data management controls, analytics, and reporting within a large-scale data environment.
  • Work closely with data analysts, risk managers, business stakeholders, and data product teams to strengthen data governance, improve data quality, and support strategic data initiatives.
  • Leverage advanced analytical skills and technologies, including Python, SQL, PySpark, Databricks, and business intelligence tools, to analyze large datasets and deliver actionable insights.
  • Design, develop, and continuously improve data management controls while independently evaluating their effectiveness and adherence to established methodologies.
  • Support data governance transformation initiatives focused on automation, preventative controls, risk management, and operational efficiency.
  • Work hands-on with large volumes of data across evolving data environments to identify trends, troubleshoot issues, and implement technical improvements.
  • Develop and enhance reporting solutions, dashboards, and analytical processes to improve transparency and support business decision-making.
  • Collaborate with cross-functional business units and data product teams to gather requirements, translate technical findings, and deliver meaningful insights.
  • Leverage AI tools and automation technologies to streamline workflows, improve efficiency, and support innovation.
  • Contribute to enterprise data products throughout the full data lifecycle, ensuring effective data management and governance practices.
  • Identify opportunities to simplify processes, strengthen controls, improve accountability, and enhance data management capabilities.
  • Share technical expertise, analytical insights, and best practices with broader data and business teams.
  • Independently troubleshoot complex data issues, proactively identify solutions, and drive initiatives with minimal supervision.

Required Qualifications

  • Strong experience as a Data Analyst or Senior Data Analyst working with large datasets in enterprise data environments.
  • Strong hands-on experience with Python and SQL for data analysis, manipulation, and automation.
  • Experience with PySpark for large-scale data processing and analytics.
  • Experience with Databricks for data engineering, analytics, or processing workflows.
  • Experience with business intelligence and reporting tools; Amazon QuickSight preferred.
  • Strong understanding of data management, data governance, data quality, and data lifecycle processes.
  • Experience developing, implementing, or improving data management controls and analytical reporting solutions.
  • Ability to analyze complex datasets, identify trends, troubleshoot data issues, and deliver actionable business insights.
  • Experience leveraging AI tools to improve productivity, streamline processes, and enhance analytical efficiency.
  • Strong communication skills with the ability to translate complex technical concepts and analytical findings to nontechnical business stakeholders.
  • Experience gathering business requirements and collaborating with cross-functional teams to deliver data-driven solutions.
  • Strong analytical, critical-thinking, and problem-solving abilities.
  • Ability to work independently, proactively identify issues, and troubleshoot technical challenges with minimal supervision.
  • Experience with process improvement, automation, and technical innovation.
  • Strong organizational skills, attention to detail, and ability to manage multiple priorities in a fast-paced environment.
  • Ability to work effectively in a collaborative, team-oriented environment.
  • Experience in financial services, risk management, or enterprise data governance environments preferred.
  • Must be able to work onsite in Richmond, VA or McLean, VA in a hybrid capacity.
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