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Key Responsibilities and Required Skills for Data Quality Specialist

💰 $80,000 - $140,000

Data QualityData GovernanceData Management

🎯 Role Definition

This role requires a Data Quality Specialist to design, implement, and operationalize a comprehensive data quality framework that ensures our business and analytical data is accurate, consistent, and trusted. This role sits at the intersection of data engineering, data governance, and business analytics: you will develop rules and controls, automate testing and monitoring, lead incident remediation, and partner with data stewards and product teams to continually improve data fitness for purpose.

Key focus areas: data profiling and discovery, authoring and operationalizing data quality rules, monitoring and alerting, root-cause analysis and remediation, metadata & lineage management, SLA/KPI tracking, and enabling a culture of data stewardship.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst with strong SQL and ETL validation experience
  • Data Steward or Data Governance Analyst who has owned data definitions and quality issues
  • ETL/Data Engineer with hands-on experience testing and validating pipelines

Advancement To:

  • Senior Data Quality Specialist / Lead Data Quality Engineer
  • Data Quality Manager / Head of Data Governance
  • Data Engineering Manager or Director of Data Platforms

Lateral Moves:

  • Data Governance Specialist
  • Master Data Management (MDM) Specialist
  • Business Intelligence / Analytics Engineer

Core Responsibilities

Primary Functions

  • Lead the full lifecycle of data quality programs: define objectives, implement data quality frameworks, operationalize rules, and measure impact against business KPIs and SLAs.
  • Design, develop and maintain automated data quality tests and validation suites for ETL pipelines, streaming sources, and data marts using SQL, Python, or dedicated DQ tools.
  • Perform detailed data profiling and exploratory data analysis to discover anomalies, outliers, and data distribution issues across source and downstream systems.
  • Author and operationalize comprehensive data quality rules, including validity, completeness, uniqueness, conformity, timeliness, and referential integrity checks.
  • Build and maintain data quality dashboards, scorecards, and trend reports using BI tools (Tableau, Power BI, Looker) to communicate quality metrics to technical and business stakeholders.
  • Implement monitoring and alerting for data quality SLA breaches, pipeline failures, and data drift using observability platforms or custom tooling.
  • Collaborate with data engineers to integrate data quality checks into CI/CD pipelines and data platform orchestration (Airflow, dbt, Prefect).
  • Manage end-to-end incident response for data quality events: triage issues, perform root-cause analysis, coordinate remediation, and document fixes to prevent recurrence.
  • Define and maintain data lineage and metadata records to identify upstream sources and downstream consumers affected by quality issues, using lineage tools or metadata catalogs.
  • Operate and administer data quality and governance tools (e.g., Informatica Data Quality, Talend, Great Expectations, Collibra, Alation, Atlan) to scale quality controls.
  • Create and maintain clear data contracts and SLAs with data producers and consumers that document expected schema, semantics, and quality thresholds.
  • Lead data stewardship efforts: assign ownership, facilitate stewardship councils, and coach business owners to resolve recurring data quality issues.
  • Implement master data workflows and deduplication/matching logic to improve entity resolution and single source-of-truth across domains.
  • Conduct impact analysis for proposed changes to schemas, ETL transformations, or business logic, proactively mitigating quality regressions.
  • Partner with privacy, security, and compliance teams to ensure data masking, anonymization, and GDPR/CCPA controls do not compromise data quality or downstream analytics.
  • Optimize and tune data quality checks for scale and performance on large datasets and cloud data warehouses (Snowflake, BigQuery, Redshift).
  • Establish repeatable templates and libraries of quality checks and remediation playbooks to accelerate onboarding to the quality program.
  • Provide subject-matter expertise to product and analytics teams on data quality best practices, including data modelling, reliable joins, and effective source system design.
  • Maintain a prioritized backlog of data quality issues and projects, and coordinate cross-functional workstreams to deliver measurable quality improvements.
  • Validate and endorse data migration and integration efforts by running pre- and post-migration quality assurance and reconciliation tests.
  • Track and report business impact of data quality improvements (reduction in incident count, time-to-resolution, financial or operational KPIs) to stakeholders and leadership.
  • Stay current with data quality tooling, patterns, and regulatory requirements and propose continuous improvements and adoption strategies.

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 analysts and data stewards in data quality methodology and tooling.
  • Assist in vendor evaluations and POC efforts for data quality, lineage, and metadata solutions.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL proficiency for complex profiling, joins, window functions, and performance-tuned queries.
  • Programming/scripting in Python (pandas, PySpark) for data validation, automation, and ETL testing.
  • Hands-on experience with data quality tools and frameworks such as Great Expectations, Informatica Data Quality, Talend, or Deequ.
  • Familiarity with data catalog/metadata/lineage solutions (Collibra, Alation, Atlan, OpenLineage).
  • Experience with cloud data warehouses and platforms: Snowflake, BigQuery, Redshift, Databricks, and associated ETL/ELT patterns.
  • Knowledge of data modeling concepts (dimensional modeling, normalization, master data management).
  • Proficiency in designing and maintaining monitoring and alerting solutions (Prometheus, Grafana, Splunk, or cloud-native monitoring).
  • Experience integrating data quality checks into CI/CD and orchestration tools (Airflow, dbt, Jenkins).
  • Working knowledge of BI and reporting tools (Tableau, Power BI, Looker) to build quality dashboards and scorecards.
  • Familiarity with data privacy, security, and regulatory compliance considerations (GDPR, CCPA) as they relate to data quality.
  • Experience in root cause analysis, data reconciliation, and transaction-level reconciliation techniques.
  • Understanding of message/streaming systems (Kafka, Kinesis) and validating streaming data quality where applicable.

Soft Skills

  • Strong stakeholder management and cross-functional collaboration; able to translate technical findings into business impact.
  • Excellent written and verbal communication for documenting rules, playbooks, and presenting quality metrics to leadership.
  • Analytical mindset and attention to detail with a pragmatic approach to prioritization and trade-offs.
  • Problem solving and investigative skills to trace issues across complex data ecosystems.
  • Project management skills and the ability to drive initiatives end-to-end with clear accountability.
  • Coaching and mentorship skills to uplift data stewardship maturity in the organization.
  • Adaptability to evolving data architectures, tools, and business requirements.
  • Customer-centric mindset with a focus on enabling reliable, trusted data for analytics and operations.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Information Systems, Data Science, Statistics, Mathematics, or a related quantitative field.

Preferred Education:

  • Master's degree in Data Science, Business Analytics, Computer Science, or a related discipline.
  • Professional certifications in data governance, data quality, or cloud platforms (e.g., Google Professional Data Engineer, AWS Certified Data Analytics, Collibra certification).

Relevant Fields of Study:

  • Computer Science / Software Engineering
  • Data Science / Analytics / Statistics
  • Information Systems / Business Information Management
  • Mathematics / Applied Mathematics

Experience Requirements

Typical Experience Range: 3–7 years working in data quality, data governance, data engineering, or analytics roles.

Preferred:

  • 5+ years of direct experience implementing data quality programs or operating as a data quality engineer/specialist.
  • Experience working in cross-functional enterprise environments, driving measurable improvements across multiple data domains.
  • Proven track record of building automated testing frameworks, operational monitoring, and demonstrating business impact through quality metrics.
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