Key Responsibilities and Required Skills for Data Management Specialist

🎯 Role Definition

A Data Management Specialist is responsible for designing, implementing and maintaining enterprise-level data management practices to ensure data is accurate, discoverable, secure, and fit-for-purpose. This role combines data governance, master data management (MDM), data quality, metadata management and strong stakeholder engagement to deliver reliable data assets that power analytics, reporting, and operational systems. The Data Management Specialist partners with business owners, data engineers, BI teams, compliance, and IT to translate business requirements into scalable data processes and tools.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Analyst with experience in data cleansing and reporting
Database Administrator or ETL Developer transitioning to governance-focused work
Master Data Analyst / MDM Analyst who has worked on data standardization projects

Advancement To:

Data Governance Manager / Lead
Data Architect or Enterprise Data Architect
Head of Data Management / Director of Data Operations

Lateral Moves:

Data Engineer (focus on pipelines and platform)
Business Intelligence/Analytics Lead
Information Security Analyst (data privacy specialization)

Core Responsibilities

Primary Functions

Lead the definition, documentation and enforcement of data governance policies, standards, and best practices across master data, reference data and metadata domains to ensure consistent, auditable data use enterprise-wide.
Design, implement and maintain master data management (MDM) processes, including record matching, survivorship rules, data consolidation, cross-system synchronization and reconciliation for customers, products, vendors and other critical entities.
Develop and operate data quality frameworks: define data quality rules, implement automated data profiling and validation checks, set SLA thresholds, monitor metrics and drive remediation with business stakeholders.
Serve as the steward for metadata: build and curate glossaries, business definitions, data lineage diagrams, attribute dictionaries and data catalogs to improve discoverability and reduce ambiguity.
Partner with data engineering and ETL teams to design robust data ingestion, transformation and orchestration patterns that preserve lineage, improve performance and minimize data drift.
Implement and manage data catalog and metadata management tools (e.g., Collibra, Alation, Informatica EDC), ensure catalog entries are complete, accurate and linked to owners and policies.
Coordinate data migration and system conversion activities, including source-to-target mapping, data cleansing, deduplication and validation during mergers, cloud migrations or application retirements.
Manage role-based access controls and data provisioning processes in coordination with IAM and security teams to enforce least-privilege and support audit and compliance requirements.
Conduct regular data audits, root-cause investigations and corrective action plans to address recurring data issues and to improve upstream system controls.
Define and report data management KPIs (e.g., data completeness, accuracy, timeliness, duplication rate) to leadership and use metrics to prioritize corrective activities.
Facilitate cross-functional data governance committees and working groups, capture decisions, drive consensus on data ownership, SLA agreements and escalation paths.
Design and execute master data onboarding processes for new products, customers or vendors, including validation rules, enrichment steps and downstream distribution.
Create and maintain operational runbooks, SOPs, and playbooks for data operations, including incident response procedures, data reconciliation steps and recovery plans.
Implement data retention, archival and purging policies to manage data lifecycle in compliance with regulatory requirements and cost objectives.
Collaborate with legal, privacy and compliance teams to interpret GDPR, CCPA and other privacy regulations and implement data handling, consent tracking and anonymization strategies where required.
Build and maintain automated data quality dashboards and alerts (Power BI, Tableau, Looker) that enable business users and operations teams to monitor data health in real time.
Author technical and non-technical documentation, including data lineage maps, transformation logic, business rules, and training materials for data stewards and end users.
Work with product managers and business stakeholders to translate business requirements into data requirements, ensure data elements are fit-for-purpose and negotiate acceptable quality thresholds.
Drive continuous improvement by evaluating and recommending data management tools and platform upgrades (MDM, DQ, metadata, workflow engines) to reduce manual interventions and increase automation.
Support API-driven data exchanges by defining payload schemas, validation rules, and error handling for reliable data integration between SaaS applications and internal systems.
Mentor and train junior data stewards and analysts on governance processes, data profiling techniques and use of data management tools to scale data stewardship capabilities.
Oversee data reconciliation processes between operational systems and reporting platforms, diagnose discrepancies and implement systemic fixes to eliminate repeat issues.
Participate in change control and release planning to ensure data model changes, migrations or ETL modifications are validated, documented and communicated to stakeholders.
Ensure data security best practices are embedded into data lifecycle processes, including encryption at rest/in transit, masking of sensitive fields and secure data transfer protocols.

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.

Required Skills & Competencies

Hard Skills (Technical)

Strong SQL skills (complex joins, window functions, performance tuning) for data profiling, validation and transformation.
Experience with Master Data Management (MDM) platforms (e.g., Informatica MDM, SAP MDG, Reltio, Profisee) and MDM design patterns.
Familiarity with data governance and metadata tools such as Collibra, Alation, Informatica EDC or open-source equivalents.
Hands-on experience with ETL/ELT tools and orchestration frameworks (Informatica PowerCenter, Talend, dbt, Apache Airflow) and designing data pipelines.
Practical knowledge of data quality tooling (e.g., Great Expectations, Talend Data Quality, Informatica DQ) and implementing automated validation rules.
Experience with cloud data platforms and warehouses (Snowflake, AWS Redshift, Google BigQuery, Azure Synapse) and cloud storage (S3, ADLS).
Understanding of data modeling (conceptual, logical, physical), entity-relationship diagrams and normalization/denormalization tradeoffs.
Proficiency in at least one scripting/programming language used for data workflows (Python, SQL + Python, or Scala).
Experience implementing data lineage, cataloging, and metadata harvesting to maintain traceability across systems.
Knowledge of data privacy, compliance and security standards (GDPR, CCPA, HIPAA) and practical techniques for anonymization, pseudonymization and consent tracking.
Familiarity with BI/visualization tools (Power BI, Tableau, Looker) to build data quality dashboards and reports.
Experience with APIs, JSON/XML schema design and integration patterns used for system-to-system data exchange.
Version control and CI/CD experience for data artifacts (Git, CI pipelines for dbt or ETL jobs).
Practical experience with data stewardship models, RACI matrices and governance council operations.

Soft Skills

Strong verbal and written communication skills to translate technical concepts into business language and to draft clear policies and documentation.
Proven stakeholder management and influencing skills to build cross-functional alignment and secure commitment for remediation actions.
Analytical mindset with attention to detail and strong problem-solving capabilities when investigating data anomalies.
Project and program management skills—able to prioritize, plan and deliver data initiatives on time and within scope.
Facilitation skills for leading governance forums, workshops and training sessions with both technical and non-technical audiences.
Adaptability and willingness to learn new data tools and processes in a fast-evolving data ecosystem.
Customer-centric approach—focus on enabling business users with timely, accurate data products.
Collaborative team player who can operate effectively across engineering, analytics, compliance and business functions.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

Master's degree in Data Science, Information Management, Business Administration (MBA) with data focus, or equivalent graduate certifications.
Professional certifications such as Certified Data Management Professional (CDMP), Collibra Data Governance, Informatica MDM, or relevant cloud certifications (AWS/Azure/GCP).

Relevant Fields of Study:

Computer Science / Software Engineering
Information Systems / Data Management
Data Science / Business Analytics
Business Administration with analytics specialization

Experience Requirements

Typical Experience Range:

3–7 years of relevant experience in data management, data governance, MDM, or data quality roles.

Preferred:

5+ years managing enterprise data domains, with demonstrable experience implementing MDM or data governance programs across multiple business units.
Experience operating in a cloud-first environment and supporting large-scale data platform migrations is a strong plus.