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Manager Enterprise Data Management Jobs (NOW HIRING)

Radiant

Senior Manager, Enterprise Data

El Segundo, CA · On-site

As the Senior Manager, Enterprise Data, you'll set the technical direction for the team, architect the data platform, own the AI strategy, and mentor engineers while ensuring compliance with security ...

Peraton

Data Manager

Bethesda, MD

The Data Analyst will support enterprise data management, integration, governance, and configuration management activities across the program. This role is responsible for ensuring enterprise data is ...

Peraton

Data Manager

Bethesda, MD · On-site

$135K - $216K/yr

The Data Analyst will support enterprise data management, integration, governance, and configuration management activities across the program. This role is responsible for ensuring enterprise data is ...

Crescent Energy

Data Governance Manager

Houston, TX · On-site

Enterprise Data Management * Lead the enterprise Master Data Management strategy and execution across key business domains including wells, assets, facilities, production, land, finance, vendors, and ...

Radiant

Senior Manager, Enterprise Data

El Segundo, CA · On-site

$171K - $233K/yr

As the Senior Manager, Enterprise Data , you'll set the technical direction for the team-architecting the data platform, owning the AI strategy, and ensuring we make the right build-vs-buy decisions.

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$45K

$115.3K

$172.5K

How much do manager enterprise data management jobs pay per year?

As of Jul 14, 2026, the average yearly pay for manager enterprise data management in the United States is $115,331.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,500.00 and $135,500.00 per year, depending on experience, location, and employer.

How does a Manager of Enterprise Data Management typically collaborate with other departments to ensure data quality and consistency across the organization?

A Manager of Enterprise Data Management works closely with various departments such as IT, business analytics, compliance, and operations to establish data governance frameworks and standardize data practices. They facilitate cross-functional meetings to align on data definitions, quality standards, and data stewardship responsibilities. This collaboration ensures that data is accurate, accessible, and secure, supporting organizational decision-making and regulatory compliance. Regular communication and training sessions are key to maintaining consistency and fostering a data-driven culture across teams.

What are the key skills and qualifications needed to thrive as a Manager Enterprise Data Management, and why are they important?

To thrive as a Manager Enterprise Data Management, you need a deep understanding of data governance, data architecture, and analytics, typically backed by a relevant degree and experience in information management. Familiarity with tools like SQL, data warehousing platforms, ETL systems, and certifications such as CDMP (Certified Data Management Professional) are commonly required. Strong leadership, strategic thinking, and excellent communication skills help drive cross-functional collaboration and align data initiatives with business goals. These capabilities are essential for ensuring data quality, compliance, and effective use of enterprise data assets to support organizational success.

What is the difference between Manager Enterprise Data Management vs Data Analyst?

AspectManager Enterprise Data ManagementData Analyst
Primary FocusOversees enterprise-wide data strategies, governance, and management policiesAnalyzes data sets to generate insights and support decision-making
Required SkillsData governance, leadership, project management, database knowledgeData analysis, visualization, statistical skills, SQL proficiency
CertificationsCDMP, DAMA, or related certifications often preferredCertified Analytics Professional (CAP), Microsoft Data Analyst Associate
Work EnvironmentEnterprise IT teams, data management departmentsBusiness units, analytics teams, data science departments

The Manager Enterprise Data Management focuses on establishing data policies and managing data assets across the organization, while Data Analysts primarily interpret data to provide actionable insights. Both roles require strong data skills, but their responsibilities and scope differ significantly.

What is an enterprise data manager?

An enterprise data manager is a professional responsible for overseeing an organization's data management strategy, ensuring data quality, security, and compliance across various departments. They often work with data governance tools, develop data policies, and collaborate with IT teams to optimize data usage and integration. Strong analytical skills and knowledge of data management frameworks are essential for this role.

What are the 5 pillars of data management?

The five pillars of data management are data quality, data governance, data architecture, data security, and data integration. For a Manager of Enterprise Data Management, understanding these pillars is essential to ensure data is accurate, consistent, secure, and effectively utilized across the organization. Mastery of these areas supports compliance, decision-making, and overall data strategy.

What does enterprise data management do?

Enterprise Data Management (EDM) involves establishing policies, processes, and tools to ensure the accuracy, consistency, security, and availability of an organization’s data across all departments. It includes data governance, data quality, data integration, and metadata management to support decision-making and compliance. Professionals in this field often work with data management platforms and require strong analytical and technical skills.

What does a Manager of Enterprise Data Management do?

A Manager of Enterprise Data Management oversees the organization’s data strategy, ensuring data is accurate, secure, and accessible across departments. They are responsible for implementing data governance policies, managing data architecture, and leading teams that handle data integration, quality, and analytics. This role is crucial for making informed business decisions and maintaining compliance with data regulations. Additionally, they collaborate with IT and business leaders to align data initiatives with organizational goals.

What is the role of a data management manager?

A data management manager oversees the organization, quality, and security of an enterprise's data assets. They develop data policies, ensure compliance with data governance standards, and collaborate with IT and business teams to optimize data usage and integrity, often utilizing tools like data management software and requiring strong analytical and leadership skills.
What cities are hiring for Manager Enterprise Data Management jobs? Cities with the most Manager Enterprise Data Management job openings:
What are the most commonly searched types of Enterprise Data Management jobs? The most popular types of Enterprise Data Management jobs are:
What states have the most Manager Enterprise Data Management jobs? States with the most job openings for Manager Enterprise Data Management jobs include:
Manager Enterprise Data Management jobs near you
Enterprise Data Architect Consultant

Enterprise Data Architect Consultant

CG Infinity

Sugar Land, TX • On-site

Full-time

Posted 24 days ago

Job description

Enterprise Data Architect Consultant
Position Summary
CG Infinity is seeking a strategic and hands-on Enterprise Data Architect to design, build, and lead the implementation of a scalable, enterprise-wide data Lakehouse. This role will be responsible for developing a modern data architecture from the ground up, integrating multiple systems and business units into a unified data ecosystem that drives analytics, reporting, and business decision-making.
The ideal candidate combines deep technical expertise with strong business acumen, capable of leading discovery efforts, defining priorities, and ensuring data solutions are aligned with organizational goals. This individual will also play a key leadership role in establishing data governance, master data management (MDM), and enterprise data standards.
Key Responsibilities
Enterprise Data Architecture & Strategy
  • Design and implement a scalable, secure, and high-performance enterprise data Lakehouse from inception.
  • Define the end-to-end data architecture, including data ingestion, transformation, storage, integration, and consumption layers.
  • Establish architectural standards, frameworks, and best practices aligned with business and technology strategies.
  • Evaluate and recommend technologies (cloud platforms, ETL/ELT tools, data lakes, Lakehouses) to support long-term scalability.
Cross-Functional Discovery & Requirements Alignment
  • Lead discovery sessions with business and technical stakeholders to identify high-value use cases, priorities, and dependencies.
  • Translate business requirements into technical data models, data flows, and architecture designs.
  • Ensure alignment between data solutions and business objectives, including KPIs, reporting, and analytics needs.
  • Develop and maintain a data roadmap with clearly defined phases, milestones, and deliverables.
Data Lakehouse Development & Delivery
  • Oversee development of integrated data pipelines that connect disparate systems (ERP, CRM, operational systems, third-party platforms).
  • Define and implement data models (conceptual, logical, physical) to support analytics and reporting.
  • Establish data quality frameworks and ensure reliability, consistency, and integrity of enterprise data.
  • Ensure performance optimization and scalability of the data environment.
Data Governance & Master Data Management
  • Develop and implement enterprise data governance policies, standards, and controls.
  • Lead Master Data Management (MDM) initiatives to standardize key business entities across systems.
  • Define data ownership, stewardship, and accountability models across business units.
  • Ensure compliance with regulatory, security, and data privacy requirements.
Leadership & Stakeholder Engagement
  • Act as a trusted advisor to executive leadership, including the CTO and business leaders.
  • Communicate complex technical concepts clearly to non-technical stakeholders.
  • Lead cross-functional teams, including data engineers, analysts, and business users.
  • Drive adoption of data solutions across the organization through change management and stakeholder alignment.

Required Qualifications
  • 8+ years of experience in data architecture, data engineering, or enterprise data management roles.
  • Proven experience building an enterprise data Lakehouse from scratch spanning multiple systems and business units.
  • Strong experience with data modeling, ETL/ELT design, and data integration frameworks.
  • Hands-on experience with cloud data platforms (e.g., Azure, AWS, or GCP).
  • Demonstrated expertise in:
    • Data Governance frameworks
    • Master Data Management (MDM)
    • Data quality and metadata management
  • Experience leading discovery sessions and requirements gathering workshops with senior stakeholders.
  • Strong understanding of enterprise systems (ERP, CRM, operational apps) and integration patterns.
  • Excellent communication, facilitation, and leadership skills.

Preferred Qualifications
  • Experience in consulting environments or multi-client, multi-business unit organizations.
  • Industry experience in oil & gas or chemical sectors, with an understanding of upstream, midstream, downstream, or refining operations.
  • Familiarity with modern data tools (e.g., Snowflake, Databricks, Azure Synapse, Power BI, Tableau).
  • Experience implementing data lakes, Lakehouse architectures, or hybrid data ecosystems.
  • Knowledge of Agile and iterative delivery methodologies.
  • Relevant certifications (e.g., Azure Data Architect, AWS Data Analytics, DAMA CDMP).

Success Metrics
  • Successful delivery of a fully operational enterprise data Lakehouse aligned with business priorities.
  • Measurable improvement in data accessibility, quality, and reporting capabilities.
  • Adoption of data governance and MDM practices across business units.
  • Delivery of a clear project roadmap with defined milestones, timelines, and outcomes.
  • Positive stakeholder feedback on alignment between business needs and technical solutions.

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