Key Responsibilities and Required Skills for Data Steward

🎯 Role Definition

As a Data Steward you will be the business-facing owner of data quality, definitions, and lifecycle for critical data domains. You will partner with data owners, engineers, analysts, and compliance teams to design and operationalize governance processes, maintain metadata and business glossaries, remediate data issues, and ensure that data is fit for analytics, reporting, and operational use. This role requires a blend of technical fluency (SQL, data profiling, metadata tools), domain knowledge, and strong stakeholder facilitation to turn governance policy into measurable data improvements.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Analyst with emphasis on data quality and reporting
Business Analyst with domain knowledge and process ownership
Data Quality Analyst or MDM Analyst

Advancement To:

Senior Data Steward / Lead Data Steward
Data Governance Manager or Head of Data Governance
Master Data Management (MDM) Lead or Data Product Owner

Lateral Moves:

Data Architect (with emphasis on modeling & lineage)
Business Process Manager / Product Owner for data-enabled products

Core Responsibilities

Primary Functions

Define, document, and maintain enterprise data stewardship policies, standards, and operating procedures to ensure consistent data definitions, naming conventions, and acceptable levels of data quality across business domains.
Lead the design and implementation of data quality rules, validation checks, and exception workflows; author technical and business data quality requirements and ensure automated monitoring is in place.
Own the development and maintenance of the business glossary and metadata catalog; collaborate with metadata tool administrators to populate attributes, definitions, owners, and lineage.
Perform regular data profiling and root cause analysis using SQL, Python, and data profiling tools to identify, quantify, and prioritize data quality issues and anomalies.
Coordinate cross-functional remediation efforts by partnering with data owners, source system teams, and ETL/engineering teams to close data quality tickets and track SLA-driven resolution.
Implement and maintain master data management (MDM) processes, including onboarding, hierarchy management, matching/merging rules, survivorship rules, and reconciliation procedures.
Establish, track, and report data quality KPIs and metrics to leadership and governance committees; design dashboards and executive summaries to measure improvements and business impact.
Create and maintain data lineage documentation for critical data flows from source systems through ETL, MDM, data lake/warehouse, and BI layers to support audits and troubleshooting.
Serve as the business subject matter expert for assigned data domains (customer, product, finance, vendor, location), translating business requirements into technical specifications and validation criteria.
Enforce data access and privacy policies by coordinating with security and privacy teams to ensure appropriate role-based access controls, masking, and GDPR/CCPA compliance for stewarded data.
Facilitate and chair data governance forums, stewardship councils, and working groups to review policy exceptions, approve new fields, and prioritize data initiatives.
Build and maintain data stewardship runbooks, playbooks, and standard operating procedures for onboarding new data sources and resolving recurring issues.
Partner with analytics and BI teams to ensure semantic consistency across reporting layers and support the creation of trusted datasets for self-service analytics.
Drive data onboarding and integration programs: define source-to-target mappings, transformation rules, acceptance criteria, and data validation gates for new data pipelines.
Manage the lifecycle of data incidents and anomalies from detection to resolution, using ticketing and data governance tools to record root cause, corrective actions, and preventive measures.
Support master data consolidation, deduplication, and enrichment initiatives; coordinate with third-party data providers and reference data sources to standardize values and codes.
Define and implement automated data quality monitoring pipelines and anomaly detection using ETL and DQ tooling (e.g., Informatica, Talend, Great Expectations) and schedule recurring checks.
Provide detailed impact analysis and change management assessments for proposed schema changes, new data feeds, or system decommissioning activities.
Create training materials, runbooks, and deliver hands-on training to business users and data owners to institutionalize stewardship responsibilities and increase data literacy.
Act as escalation point for critical data issues affecting operational systems or analytics, liaising with incident response, engineering, and product teams to prioritize fixes.
Support data privacy assessments and data retention policies by identifying personally identifiable information (PII) and implementing classification and handling rules.
Partner with architects and engineers to validate data models and normalization decisions; review ERDs and physical models for stewardship compliance.
Evaluate and recommend data governance and stewardship tools (data catalogs, lineage, DQ tooling, MDM platforms); participate in vendor evaluations and proof-of-concepts.
Track regulatory and industry changes affecting data usage and retention; update stewardship policies accordingly and coordinate required remediation and reporting.

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.
Document use cases and success stories demonstrating business value delivered by stewardship actions.
Assist with periodic audits and compliance reporting, supplying lineage, glossary, and data quality evidence to auditors.

Required Skills & Competencies

Hard Skills (Technical)

Data governance frameworks and best practices (policy creation, stewardship operating model, RACI)
Metadata management and data catalog tools (Collibra, Alation, Informatica EDC, AWS Glue)
Master Data Management (MDM) concepts and platforms (Informatica MDM, Stibo, Orchestra Networks)
Data quality technologies and practices (profiling, cleansing, validation, DQ tooling like Informatica DQ, Talend, Great Expectations)
Strong SQL for profiling, lineage tracing, and ad-hoc analysis
Scripting for data analysis and automation (Python, PySpark, or R)
Data modeling fundamentals (logical/physical models, canonical models, ERDs)
Knowledge of ETL/ELT processes and tools (Informatica, Talend, Azure Data Factory, AWS Glue)
Data lineage and impact analysis techniques and tools
Familiarity with privacy & compliance regulations (GDPR, CCPA) and techniques for PII identification and masking
Experience with BI and analytics platforms (Tableau, Power BI, Looker) to create data quality dashboards
Experience with ticketing and issue-tracking systems (Jira, ServiceNow) for data issue lifecycle management
Understanding of cloud data platforms and storage (Snowflake, Redshift, BigQuery, Azure Synapse)

Soft Skills

Strong stakeholder management and cross-functional collaboration skills
Excellent written and verbal communication; able to translate technical concepts into business language
Critical thinking and systematic problem solving to identify root causes and preventive controls
Attention to detail and quality orientation when defining and enforcing data standards
Influencing and negotiation skills to build consensus across competing priorities
Project and time management with the ability to balance multiple domains and escalations
Coaching and training aptitude to upskill business users and embed stewardship responsibilities
Strategic mindset with the ability to align stewardship activities to business outcomes
Resilience and adaptability to operate in changing data landscapes and agile teams

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

Master’s degree in Data Management, Information Systems, Business Analytics or an MBA with data/governance emphasis.
Professional certifications such as CDMP (Certified Data Management Professional), DGSP (Data Governance and Stewardship Professional), or vendor MDM certifications.

Relevant Fields of Study:

Data Science / Analytics
Information Systems / Computer Science
Business Analytics / Business Administration
Statistics / Mathematics
Finance / Operations (for domain-specific stewardship)

Experience Requirements

Typical Experience Range: 3–7 years of data stewardship, data governance, or data quality experience; domain experience counts.

Preferred:

5+ years working in enterprise data governance, MDM, or data quality roles.
Demonstrable experience implementing data governance practices, metadata management, and data quality monitoring in mid-to-large organizations.
Hands-on experience with SQL and at least one scripting language (Python/PySpark), plus exposure to metadata/catalog tooling and MDM platforms.
Proven ability to work with legal/privacy teams on GDPR/CCPA compliance and to support audit requests.
Prior experience partnering with analytics, engineering, and product teams to operationalize data policies and produce measurable business outcomes.