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Data Steward Jobs in Virginia (NOW HIRING)

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$71

How much do data steward jobs pay per hour?

As of Sep 8, 2026, the average hourly pay for data steward in Virginia is $35.94, according to ZipRecruiter salary data. Most workers in this role earn between $24.09 and $39.33 per hour, depending on experience, location, and employer.

What is a data steward?

A data steward’s primary responsibility is to ensure data quality for an organization. In this job, your duties include documenting data and metadata, determining procedures for data interpretation, and working closely with other data analysts and managers to ensure that data is uniform for internal and external users. The qualifications for this career include a bachelor’s degree in statistics, computer science, or a closely related field. You also need several years of related job experience, for example, in data storage or extraction. It’s also helpful to have programming expertise and technical writing skills.

What are the typical challenges faced by data stewards when ensuring data quality across multiple departments?

Data Stewards often encounter challenges related to inconsistent data standards and varying data management practices across departments. Coordinating with different teams to align on data definitions, formats, and quality expectations can be complex, especially in large organizations. Additionally, they may need to address resistance to change or limited data literacy among colleagues, requiring effective communication and training. Successful Data Stewards proactively foster collaboration and advocate for standardized data governance processes to overcome these hurdles and maintain high data quality.

What are the key skills and qualifications needed to thrive as a data steward, and why are they important?

To thrive as a Data Steward, you need a solid understanding of data management principles, data governance frameworks, and strong analytical skills, often supported by a degree in information systems or a related field. Familiarity with data quality tools, metadata management systems, and industry standards like GDPR or HIPAA compliance is typically essential. Attention to detail, strong communication, and stakeholder management skills help Data Stewards effectively enforce data policies and collaborate across departments. These skills and qualities are crucial for maintaining data integrity, ensuring compliance, and enabling data-driven decision-making within organizations.

What is the difference between Data Steward vs Data Analyst?

AspectData StewardData Analyst
Primary RoleManages data quality, governance, and complianceAnalyzes data to generate insights and support decision-making
Skills & CertificationsData governance, data management, certifications like CDMPData analysis, SQL, visualization tools, certifications like CAP or Microsoft certifications
Work EnvironmentOften within data governance or data management teamsBusiness units, analytics teams, or BI departments
Industry UsageCommon in organizations with data governance frameworksWidespread across industries for data-driven decision-making

While Data Stewards focus on maintaining data quality and governance, Data Analysts interpret data to support business insights. Both roles require data skills but serve different functions within an organization.

More about Data Steward jobs

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Data Steward jobs near you
Infographic showing various Data Steward job openings in Virginia as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution, with an average salary of $74,764 per year, or $35.9 per hour.

Exploitation Specialist/Data Steward 08

Arlington, VA • On-site

Select Search Associates LLC
See more jobs at Select Search Associates LLCexternal

Full-time

Re-posted 19 days ago

Key responsibilities

  • Design, develop, and maintain scalable data pipelines to ingest, process, and store data from emerging sensors and collection platforms.

  • Create and manage database schemas, tables, and supporting infrastructure while implementing automated data quality monitoring and health alerts.

  • Develop preprocessing and standardization workflows to prepare data for AI/ML model training, testing, and evaluation.

Job description

Overview

We are seeking an experienced Explotation Specialist/Data Steward to join a high-performing team supporting advanced AI and machine learning initiatives for complex national security and intelligence missions.

This program focuses on evaluating AI models against Government datasets to assess their performance, resilience, and robustness across a broad spectrum of adversarial scenarios. The effort also includes testing and validating autonomous algorithms designed to support operational decision-making in dynamic mission environments.

As a Data Scientist, you will play a key role in designing, implementing, and maintaining the data architecture that supports AI/ML model development, testing, and evaluation. You will develop scalable data pipelines, integrate data from emerging sensors and platforms, and ensure data is standardized, accessible, and compliant with Government data governance requirements. Working closely with engineers, imagery scientists, and mission analysts, you will help deliver reliable, high-quality datasets that enable advanced analytics and operational decision-making.

Required Qualifications
  • 10+ years of relevant experience. (A combination of years of experience, professional certifications, and education may be used in lieu of years of experience)
  • Demonstrated understanding of GIS, data schemas, and the geo-referencing of information
  • Knowledge of extraction and metadata specifications
  • Knowledge of Relational Database Management System (RDBMS) architectures, best practices for data normalization, and performance tuning/optimization techniques
  • Knowledge of enterprise geodatabases, such as Spatial Database Engines (SDEs)
  • Knowledge of tools such as the ArcGIS Suite, ArcGIS Online, Portal, Excel, Access, and SQL for the storage, organization, and maintenance of spatial data
  • Familiarity with ontology concepts as they relate to information systems, data modeling, and data relationships
  • Strong collaboration skills with the ability to work effectively as a team player across technical and mission-focused teams
  • Knowledge of scripting languages, such as R or Python
Preferred Qualifications
  • Experience supporting NGA enterprise technologies and environments, including NGA CORE, NGA MLOps, Government Authority to Operate (ATO) processes, and Government Non-Person Entity (NPE) certificates.
  • Experience implementing processes and tools within organizations utilizing the Scaled Agile Framework (SAFe).
  • Experience developing dashboards and data visualizations to monitor operational pipeline health, data availability, currency, throughput, and overall system performance.
Key Responsibilities
  • Design, develop, and maintain scalable data pipelines to ingest, process, and store data from emerging sensors and collection platforms.
  • Create and manage database schemas, tables, and supporting infrastructure while implementing automated data quality monitoring and health alerts.
  • Develop API-based workflows to securely move sensor data between Government data platforms in accordance with program requirements.
  • Ensure all sensor data complies with Government-defined schemas, formatting standards, metadata requirements, and data governance policies.
  • Assess the impact of new sensor data on existing databases, metadata models, APIs, schemas, and ETL processes to support seamless integration into operational data pipelines.
  • Develop preprocessing and standardization workflows to prepare data for AI/ML model training, testing, and evaluation. Activities may include format conversion, imagery chipping, orthorectification, metadata normalization, and other transformation processes required to produce high-quality analytical datasets.
  • Collaborate with cross-functional teams to optimize data workflows, improve pipeline reliability, and support the delivery of mission-ready datasets for advanced analytics and machine learning applications.