Data Manager Role
- Salary
- Not stated
- Level
- Lead
- Work type
- Not stated
- Visa
- Not stated
Open. First seen 6 October 2026.
The team: Roles HIRES STEADILY
This role is one of 20 open in Roles. 2 were posted in the last 30 days and the oldest in August: a team that hires all year rather than one being built.
WHAT THE TEAM WORKS WITH, ACROSS ITS POSTINGS
SQL (9 of 20), Python (7 of 20), Generative AI (4 of 20), JavaScript (4 of 20), Machine Learning (4 of 20), RAG (4 of 20), AWS (3 of 20)
OTHER ROLES ON THIS TEAM
- Data Architect Role posted Oct 16
- Database Administrator Role posted Oct 16
- Data Engineer Role posted Sep 11
- Data Analyst Role posted Sep 11
- AI Engineer Role posted Sep 11
- Cloud Infrastructure Engineer Role posted Sep 11
- Data Scientist Role posted Aug 8
- DevSecOps Engineer Role posted Aug 8
- Data Product Owner Role posted Aug 8
- Data Visualization Specialist Role posted Aug 8
- Data Program Manager Role posted Aug 8
- Full-Stack Developer Role posted Aug 8
- Generative AI Engineer Role posted Aug 8
- Data Security Specialist Role posted Aug 8
- Database Developer Role posted Aug 8
- Machine Learning Engineer Role posted Aug 8
- Software Engineer (Back End and Cloud) Role posted Aug 8
- Data Strategist Role posted Aug 8
- Front-End Developer Role posted Aug 8
From the team and dates on OpenDataJobs's own job board.
About the role
The work
Data Managers run the recurring operations that keep data accurate, current, usable, and available to the people who depend on it. They manage intake, validation, correction, publication, issue handling, service routines, and the handoffs between data producers, technical teams, stewards, and consumers.
This is an operating role with responsibility for making the data lifecycle work in practice. Data Managers use metrics, controls, and workflow improvements to reduce avoidable errors and delays, while coordinating with Data Governance Specialists on standards, Metadata Management Specialists on context, and Data Inventory Managers on enterprise coverage. They own execution and continuous improvement of day-to-day data operations.
What you'll build
· Data-intake, validation, correction, and publication workflows with clear owners and service expectations.
· Data-quality checks, reconciliations, issue queues, and root-cause improvement routines.
· Operational dashboards and reporting that show timeliness, completeness, accuracy, and unresolved issues.
· Working agreements and handoffs among data producers, stewards, engineers, and data consumers.
· Repeatable processes for maintaining datasets used in reporting, analytics, or artificial intelligence applications.
Who you are
You see recurring work as a system to improve. You can establish a reliable cadence, spot weak handoffs, and use evidence to make data operations more dependable without turning every exception into a new manual process.
You can move between detail and coordination. You are comfortable inspecting data, resolving issues with the people closest to the source, and explaining operational performance to leaders who need a clear view of risk and progress.
What you bring
· Experience operating data-quality, data-lifecycle, publication, or data-service processes.
· Working ability with structured data, SQL, validation methods, and reconciliation techniques.
· Experience with workflow design, issue management, service metrics, and continuous improvement.
· Ability to coordinate data producers, technical teams, stewards, and consumers.
· Clear judgment about data accuracy, timeliness, traceability, and operational risk.
About OPEN Data Jobs
OPEN Data Jobs connects AI, data, and software professionals with critical roles, primarily in the federal sector. Registering with ODJ can put your profile in view for multiple positions across several clients.
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Requirements
What openings may require
Openings may specify a data domain, reporting cycle, platform, data-quality tool, or leadership scope. Some may ask for operational responsibility over a team, while others may emphasize hands-on work managing high-volume data intake, remediation, and publication.
Data-intensive artificial intelligence work may require managers who can maintain reliable source datasets, document changes, and coordinate quality controls across the lifecycle. This does not make the Data Manager the owner of the artificial intelligence model, governance policy, metadata standard, or enterprise inventory unless the opening expressly combines those responsibilities.
Benefits
Compensation, benefits, work location, and employment terms are set for each specific opening and will be stated with that opening
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