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Key Responsibilities and Required Skills for Data Manager

💰 $70,000 - $140,000

Data ManagementAnalyticsData Governance

🎯 Role Definition

The Data Manager is responsible for owning the quality, accessibility, security, and lifecycle of organizational data. This role combines data governance, data operations, and stakeholder engagement to deliver trusted datasets for analytics, reporting, and business decisions. A successful Data Manager designs and enforces data standards, manages ETL/data pipelines and master data, implements data quality frameworks, and partners with engineering, analytics, and business teams to ensure reliable, compliant, and performant data assets.

Keywords: Data Manager, data governance, data quality, ETL, data warehouse, master data management, BI, SQL, cloud data platforms, metadata management, data stewardship.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Data Analyst with data stewardship responsibilities
  • ETL Developer / Data Engineer transitioning into operations and governance
  • Business Intelligence (BI) Analyst or Reporting Lead with cross-functional domain knowledge

Advancement To:

  • Senior Data Manager / Lead, Data Management
  • Head of Data Operations or Director of Data Governance
  • Data Governance Lead / Data Strategy Lead
  • Chief Data Officer (CDO) in larger organizations

Lateral Moves:

  • Data Architect (for technically oriented managers)
  • Analytics Manager / Business Intelligence Manager
  • Master Data Management (MDM) Lead

Core Responsibilities

Primary Functions

  • Develop, document, and operationalize an enterprise data governance program including policies, standards, data ownership, stewardship responsibilities, and data lifecycle processes to ensure consistent, reusable, and auditable data across systems.
  • Define, implement and monitor data quality metrics, KPIs and SLAs (completeness, accuracy, timeliness, consistency, and uniqueness); create automated checks and remediation workflows and report quality trends to business stakeholders.
  • Own master data management (MDM) processes and reference data strategies including entity resolution, deduplication rules, survivorship logic, and ongoing maintenance to ensure a single source of truth for critical business domains (customers, products, suppliers).
  • Design, build, and maintain ETL/ELT pipelines and data ingestion processes (batch and streaming) in collaboration with data engineering to ensure reliable delivery of clean, modeled datasets into the data warehouse/lake.
  • Lead data integration and system-to-system data synchronization projects, mapping source-to-target data flows, validating transformations, and coordinating cutovers and reconciliation testing during migrations or system upgrades.
  • Serve as the primary owner for the enterprise data catalog and metadata management program: catalog datasets, maintain a business glossary, record lineage, classification, and usage metadata to improve discoverability and trust.
  • Implement data access controls, role-based access, and policies in coordination with security, IAM and cloud teams to ensure least-privilege access, protect sensitive data, and enforce data masking where required.
  • Manage data incident response and root cause analysis: triage data incidents, coordinate cross-team remediation, document corrective actions, and update preventative controls to reduce recurrence.
  • Collaborate with analytics, product, and business domain owners to translate analytic and reporting requirements into curated datasets, dimensional models, and semantic layers that support BI and ML use cases.
  • Establish and enforce versioning, release management, and deployment processes for schema changes and data model updates, including migration testing, rollback plans, and stakeholder communication.
  • Develop and maintain data dictionaries and canonical data models for primary business entities, documenting definitions, accepted values, transformations, and business context to reduce ambiguity across teams.
  • Monitor and optimize database/table performance and storage usage in data warehouses (Snowflake, BigQuery, Redshift) and related systems; implement partitioning, clustering, and indexing strategies to improve query performance and cost efficiency.
  • Lead vendor/third-party data provider relationships: evaluate data vendors, negotiate SLAs, manage onboarding and quality validation processes, and govern the ongoing use of external datasets.
  • Plan and oversee data retention, archiving, and deletion policies across transactional and analytical systems to meet business needs, reduce storage footprint, and comply with regulatory requirements.
  • Create and maintain operational reports, dashboards and runbooks that provide visibility into pipeline health, data quality trends, and SLA adherence for technical and business audiences.
  • Architect and enforce data privacy & compliance controls (GDPR, CCPA, HIPAA as applicable): implement data classification, consent tracking, subject access request support, and retention controls in collaboration with legal and compliance teams.
  • Lead and mentor a team of data stewards, analysts, or operations specialists: set objectives, conduct performance reviews, coordinate staffing, training, and career development in the data management function.
  • Develop test plans, conduct data validation and reconciliation during system implementations and releases, ensuring reconciliation between source systems and reporting layers with documented sign-off processes.
  • Coordinate cross-functional project delivery as the focal point for data-related workstreams in agile programs: define acceptance criteria, maintain backlog items, and participate in sprint planning and reviews.
  • Conduct cost/benefit analysis and prioritization for data product roadmaps; recommend investments in automation, tooling (data catalog, DQ platforms, MDM solutions) and cloud services to improve operational efficiency.
  • Maintain comprehensive documentation (architectural diagrams, SOPs, runbooks) and provide training to business users and analytics teams on data definitions, processes, and self-service capabilities.
  • Drive continuous improvement initiatives by identifying manual, error-prone processes and implementing automation (Airflow, orchestration tools, CI/CD for data pipelines) to reduce operational toil and increase reliability.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Facilitate cross-functional data governance forums, working groups and training sessions to increase data literacy and alignment across the company.
  • Evaluate and pilot new data management tools and frameworks (data catalogs, DQ engines, MDM platforms) and provide recommendations for enterprise adoption.
  • Assist with budget planning, vendor contracts, and procurement related to data platforms and data services.
  • Provide executive-level reporting and briefings on data health, risks, and program outcomes.

Required Skills & Competencies

Hard Skills (Technical)

  • Strong SQL expertise for data profiling, validation, complex joins, window functions, and performance tuning across large datasets.
  • Hands-on experience with data warehouse platforms (Snowflake, BigQuery, Redshift, Azure Synapse) and knowledge of cost/performance tradeoffs.
  • Practical knowledge of ETL/ELT tools and orchestration (Apache Airflow, dbt, Talend, Informatica, Azure Data Factory) and pipeline monitoring best practices.
  • Master Data Management (MDM) design and implementation experience including entity resolution and survivorship logic.
  • Data quality tooling and frameworks experience (Great Expectations, Deequ, Informatica DQ) and the ability to operationalize automated DQ checks.
  • Metadata management and data catalog experience (Collibra, Alation, Amundsen) and building business glossaries and lineage.
  • Programming/scripting proficiency in Python or Scala for automation, data transformation, and tooling integration.
  • Familiarity with BI and visualization tools (Tableau, Power BI, Looker) and building semantic models for analytics consumers.
  • Understanding of data security, privacy controls, and compliance requirements (GDPR, CCPA, HIPAA) and methods for pseudonymization/masking.
  • Cloud platform experience (AWS, GCP, Azure) and familiarity with cloud storage, IAM, and managed database services.
  • Proven ability to design data models (3NF, star schema, dimensional modeling) and produce data dictionaries.
  • Knowledge of version control (Git), CI/CD practices for data pipelines, and infrastructure-as-code concepts for data environments.
  • Experience with monitoring/observability tools for pipelines and data stores; ability to set up alerts, dashboards and SLOs.

Soft Skills

  • Strong stakeholder management and ability to influence cross-functional teams (product, engineering, compliance, business owners).
  • Excellent written and verbal communication skills; able to translate technical concepts to non-technical audiences.
  • Detail-oriented with strong organizational skills and a documented approach to process and SOP creation.
  • Analytical problem-solver comfortable with ambiguity and complex root-cause investigations.
  • Leadership and team-building capabilities: hiring, mentoring, and performance management of data operations staff.
  • Project and program management skills; capable of prioritizing competing requests and delivering on time.
  • Customer-service mindset with emphasis on timeliness, transparency, and continuous improvement.
  • Change management and training aptitude to drive adoption of new data standards and tools.
  • Business acumen and ability to align data initiatives with measurable business outcomes and KPIs.
  • Resilience and adaptability in fast-paced, evolving technical environments.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Information Systems, Data Science, Statistics, Business Analytics, or a closely related field.

Preferred Education:

  • Master’s degree in Information Systems, Data Science, Business Analytics, or an MBA with strong analytics focus.
  • Professional certifications such as CDMP (Certified Data Management Professional), DGSP (Data Governance and Stewardship Professional), or cloud certifications (AWS/GCP/Azure) are highly desirable.

Relevant Fields of Study:

  • Computer Science / Software Engineering
  • Information Systems / IT Management
  • Data Science / Statistics / Applied Math
  • Business Analytics / Economics

Experience Requirements

Typical Experience Range:

  • 3–8+ years in data-focused roles with demonstrated progression (Data Analyst, ETL Developer, Data Engineer, Data Steward) or 5+ years for mid-to-senior manager responsibilities.

Preferred:

  • 5+ years managing data governance, MDM or data operations functions with hands-on technical delivery experience.
  • Proven track record running cross-functional data programs, implementing data quality frameworks and collaborating with engineering and compliance teams in production environments.
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