Key Responsibilities and Required Skills for Data Management Analyst

🎯 Role Definition

This role requires an experienced Data Management Analyst to own the integrity, availability, and usability of enterprise data assets. The Data Management Analyst will implement and operationalize data governance and data quality initiatives, maintain metadata and data catalogs, support ETL and ingestion workflows, and partner with business stakeholders and engineering teams to ensure data is trusted, discoverable, and fit for analytical and operational use. This role requires strong SQL and scripting skills, hands-on experience with data governance or MDM tools, and a pragmatic approach to translating business requirements into repeatable data management processes.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Analyst with strong data quality responsibilities
Business Analyst / BI Analyst with experience in data modeling and governance
Database Administrator or ETL Developer transitioning to governance-focused work

Advancement To:

Senior Data Management Analyst
Data Governance Lead / Manager
Data Architect or Enterprise Data Manager

Lateral Moves:

Data Steward / Domain Data Owner
Business Intelligence / Analytics Lead

Core Responsibilities

Primary Functions

Develop, document, and enforce end-to-end data governance processes and standards, including policies for data ownership, stewardship, access control, retention, and lifecycle management to ensure compliance and consistent data usage across the organization.
Design and maintain data quality frameworks and scorecards that define business rules, validation logic, and KPIs; implement automated monitoring and alerting to detect, prioritize, and remediate data quality issues across source systems and downstream analytics.
Lead metadata management initiatives by building and curating a centralized data catalog, documenting datasets, tables, fields, data lineage, business definitions, and transformation logic to improve discoverability and accelerate trusted analytics.
Perform comprehensive data profiling and root-cause analysis to quantify accuracy, completeness, consistency, uniqueness, and timeliness issues; produce actionable reports and remediation plans in collaboration with source system owners.
Define, implement, and govern master data management (MDM) processes for critical domains (e.g., customer, product, vendor), including entity resolution, golden record creation, matching rules, and synchronization across systems.
Translate complex business requirements into concrete data models, schema designs, and ingestion specifications; collaborate with data engineers to ensure correct implementation of transformations and mappings in ETL/ELT pipelines.
Execute and validate ETL/ELT processes, monitor pipeline health, and coordinate incident response for data ingestion failures; author test plans and acceptance criteria for data delivery to analytics and reporting platforms.
Maintain and enrich data lineage documentation using automated tools and manual curation so that technical and non-technical stakeholders can trace data from source to consumption for audits and troubleshooting.
Implement role-based data access controls and participate in access reviews, ensuring sensitive data is classified and protected as per company policy and regulatory requirements (e.g., GDPR, CCPA, HIPAA as applicable).
Partner with business units and analytics teams to prioritize data improvements and champion data stewardship programs, establishing clear RACI models and SLA expectations for issue resolution and dataset onboarding.
Design and operate data onboarding processes that evaluate source readiness, map fields to canonical models, define transformation logic, and validate sample loads before production deployment.
Establish and run regular data governance councils, working groups, and training sessions to socialize standards, solicit feedback, and drive adoption of data best practices across product, engineering, and business functions.
Build and maintain automated reports and dashboards that communicate data quality trends, governance metrics, and data product health to senior leadership and operational teams.
Create and maintain comprehensive documentation (runbooks, SOPs, checklists) for recurring data management tasks, including data refresh schedules, drift detection, and remediation workflows to reduce operational risk.
Conduct impact analysis for proposed schema changes, data model revisions, or new data sources, coordinating testing and migration plans to minimize downstream disruptions to analytics and reporting.
Evaluate, recommend, and help implement data governance, catalog, MDM, and data quality tools (e.g., Collibra, Alation, Informatica, Talend, Great Expectations) and integrations with cloud platforms and data warehouses.
Maintain enterprise metadata standards including naming conventions, data dictionaries, and taxonomy governance to improve consistency and automated discovery in self-service analytics environments.
Lead periodic data audits and compliance assessments to validate retention, lineage, and access controls; prepare evidence and coordinate with legal and audit teams for regulatory reviews.
Act as a subject-matter expert on data provenance and data lineage when responding to ad-hoc inquiries from analytics, compliance, or operational teams seeking to validate reporting logic or data health.
Set up and refine data validation test suites for unit, integration, and regression testing of data pipelines; integrate tests into CI/CD workflows where applicable to enforce quality gates.
Drive continuous improvement by identifying opportunities for data normalization, deduplication, enrichment, and automation to reduce manual rework and increase analytical confidence.
Facilitate cross-functional workshops to align on business definitions, KPI calculations, and canonical data models to ensure consistent interpretation of metrics across teams.
Manage vendor and third-party data provider relationships; validate incoming feeds, reconcile discrepancies, and negotiate SLAs and formats to ensure reliable downstream consumption.

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.
Mentor junior data stewards and analysts on data governance practices, data profiling techniques, and use of cataloging tools.
Assist in cost optimization and resource planning for data storage, retention policies, and archival strategies in cloud data platforms.
Help define and maintain data retention and archive policies; coordinate archival runs and verification to balance compliance and performance needs.
Participate in vendor evaluations, proof-of-concepts, and pilot implementations to advance the organization’s data management capabilities.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL (complex joins, window functions, performance tuning) for data profiling, validation, and troubleshooting.
Experience with data governance and metadata management tools (e.g., Collibra, Alation, Informatica EDC, Apache Atlas) including catalog configuration and stewardship workflows.
Hands-on experience with data quality tools and frameworks (e.g., Great Expectations, Talend Data Quality, Informatica Data Quality) to create automated test suites and scorecards.
Familiarity with ETL/ELT tools and orchestration platforms (e.g., dbt, Airflow, Talend, Azure Data Factory, AWS Glue) and an understanding of pipeline design patterns.
Proficiency with scripting languages (Python, Scala, or R) for data transformation, automation, profiling, and building lightweight data utilities.
Strong understanding of data modeling and schema design (3NF, dimensional models, star/snowflake schemas) and experience producing logical and physical data models.
Experience with cloud data platforms and warehouses (e.g., Snowflake, BigQuery, Redshift, Databricks) and knowledge of cloud storage & security best practices.
Knowledge of master data management (MDM) concepts and tools, entity resolution, and golden record strategies.
Experience documenting and visualizing data lineage and impact analysis using tools or diagrams for audit and compliance purposes.
Familiarity with regulatory requirements and data privacy frameworks (GDPR, CCPA, HIPAA) and techniques for data anonymization, masking, and secure handling of PII.
Competence in BI/analytics tools (e.g., Tableau, Power BI, Looker) to validate downstream reports and support stakeholder-facing analytics.
Experience with REST APIs, data ingestion from external providers, and file-based integrations (CSV, JSON, Parquet, Avro).

Soft Skills

Strong stakeholder management — able to collaborate with product owners, engineers, legal, and business users to align priorities and resolve conflicts.
Excellent verbal and written communication skills for documenting policies, facilitating governance meetings, and presenting complex lineage/quality findings to non-technical audiences.
Analytical mindset with strong problem-solving skills and an ability to perform root-cause analysis under pressure.
Attention to detail and high level of accuracy when documenting metadata, configuring data rules, and validating datasets.
Project management and organizational skills to run data onboarding, remediation sprints, and governance initiatives end-to-end.
Influencing and negotiation skills to drive adoption of data standards and secure commitment from data owners and system teams.
Ability to prioritize work in a fast-paced environment and balance tactical fixes with strategic improvements.
Collaborative team-player attitude with coaching/mentoring experience to grow data stewardship capabilities across the organization.
Ethical judgment and a strong sense of confidentiality when working with sensitive or regulated data.
Adaptability and continuous learning mindset to evaluate emerging tools, cloud technologies, and governance frameworks.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

Master’s degree in Data Science, Information Management, Business Analytics, or MBA with analytics emphasis.
Certifications such as CDMP (Certified Data Management Professional), DGSP, Collibra Certified, or cloud certifications (AWS/Azure/GCP) are a plus.

Relevant Fields of Study:

Computer Science / Software Engineering
Information Systems / IT Management
Data Science / Statistics / Applied Mathematics
Business Analytics / Operations Research

Experience Requirements

Typical Experience Range: 3 - 7 years in data management, data governance, data engineering, or related analytics roles.

Preferred:

5+ years experience implementing data governance, data quality, MDM, or metadata management programs in medium to large enterprises.
Domain experience in regulated industries (finance, healthcare, telecom) and familiarity with compliance requirements.
Proven track record working with cloud data platforms, modern ETL/ELT tooling, and implementing automation for data validation and lineage.
Demonstrated experience leading stakeholder-facing governance councils and delivering measurable improvements in data quality and discoverability.