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Key Responsibilities and Required Skills for Data Quality Analyst

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DataQualityAnalyticsGovernance

🎯 Role Definition

A Data Quality Analyst is responsible for defining, implementing, and operating data quality programs that ensure the integrity, accuracy, and reliability of enterprise data across analytics and operational systems. This role combines hands-on data validation with stakeholder collaboration to design quality rules, automate monitoring, investigate data incidents, and enable scalable data governance practices that support reporting, ML, regulatory compliance, and business decision-making.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst with experience in data profiling and SQL-based validation.
  • ETL/BI Developer familiar with data pipelines and transformation logic.
  • QA Analyst or Test Engineer who has worked on test automation for data systems.

Advancement To:

  • Senior Data Quality Analyst / Data Quality Engineer
  • Data Quality Lead or Manager
  • Data Governance Manager or Head of Data Governance
  • Data Engineering or Analytics Engineering Lead

Lateral Moves:

  • Data Steward
  • Data Catalog / Metadata Manager
  • Business Intelligence / Analytics Manager

Core Responsibilities

Primary Functions

  • Define, document, and operationalize a data quality framework that includes measurable KPIs (completeness, accuracy, timeliness, uniqueness, consistency, validity) and quality gates for source, staging, and production data assets.
  • Design, implement, and maintain automated data quality rules and validations across ETL/ELT pipelines using SQL, Python, Great Expectations, Deequ, or vendor tools to detect anomalies and prevent bad data from propagating.
  • Perform regular data profiling and statistical analysis on datasets (structured and semi-structured) to establish baselines, identify patterns, discover anomalies, and recommend remediation.
  • Develop end-to-end data quality monitoring and observability dashboards (e.g., Looker, Tableau, Power BI, Grafana) that provide stakeholders with current health, trend analysis, and SLA compliance for critical data products.
  • Triage, investigate, and resolve data incidents by conducting root cause analysis of upstream processes, transformations, and source systems, and coordinate fixes with data engineering, product, and business teams.
  • Create and enforce data validation test suites for new data feeds, schema changes, and deployments as part of CI/CD pipelines to reduce production defects and regression issues.
  • Implement and maintain data lineage and impact analysis to trace quality issues to source systems, transformation logic, and downstream consumers to support faster investigations and change management.
  • Develop and manage data contracts and service-level agreements (SLAs) with source system owners that define quality expectations, schemas, delivery cadence, and failure handling.
  • Build and maintain reusable validation libraries, scripts, and templates to standardize checks across domains and accelerate onboarding of new datasets into the quality program.
  • Collaborate with data governance, stewardship, analytics, and product owners to prioritize data quality issues based on business impact, cost of remediation, and risk.
  • Conduct sampling, reconciliation, and record-level matching to verify completeness and accuracy of data ingestions between source systems and data warehouses/lakes.
  • Lead root-cause remediation projects that include schema corrections, transformation fixes, and source data quality improvements; coordinate QA and release activities to productionize fixes.
  • Manage and maintain metadata, catalog entries, and quality annotations in the data catalog (e.g., Collibra, Alation) to support discoverability and transparent quality status for data consumers.
  • Perform PII detection, sensitivity labeling, and quality checks aligned with data privacy and regulatory requirements (GDPR, CCPA, HIPAA) to ensure compliant data usage.
  • Create and maintain comprehensive documentation — playbooks, runbooks, incident postmortems, data quality metrics definitions, and onboarding guides — to institutionalize best practices.
  • Participate in cross-functional planning and sprint activities to incorporate quality requirements into feature development, schema evolution, and data platform initiatives.
  • Provide day-to-day production support for data quality alerts and escalations, including on-call rotation, incident tracking, and communication to impacted stakeholders.
  • Conduct training and enablement sessions for data stewards, analysts, and business users to raise awareness of data quality expectations, tools, and remediation workflows.
  • Implement and tune anomaly detection, statistical tests, and threshold-based alerts to proactively surface sudden shifts in data distributions, volume, or key business metrics.
  • Evaluate, select, and integrate third-party data quality and observability solutions (commercial or open-source) and recommend architectural patterns to scale quality controls.
  • Validate and test data transformations and aggregations by creating regression tests, lineage checkpoints, and reconciliation reports prior to production releases.
  • Monitor and report monthly/quarterly data quality KPIs to senior management and partners, including trend analysis, root cause breakdown, and remediation progress.
  • Partner with data engineers to ensure quality checks are embedded early in the data ingestion and transformation lifecycle (shift-left testing) and that fixes are automated where possible.
  • Carry out ad hoc data investigations for analytics teams, model owners, and compliance auditors to support decision-making, audits, and regulatory requests.
  • Ensure that data quality controls are aligned with data modeling standards, master data management (MDM) initiatives, and enterprise data architecture principles.

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.
  • Assist in vendor evaluations and proof-of-concepts for data quality, observability, and catalog solutions.
  • Mentor junior analysts and new hires on data quality processes, tooling, and best practices.

Required Skills & Competencies

Hard Skills (Technical)

  • Expert-level SQL for complex joins, window functions, aggregations, and performance-optimized queries for profiling and validation.
  • Proficient in Python (Pandas, PySpark) or Scala for building automated validation pipelines, ETL tests, and sampling scripts.
  • Hands-on experience with data quality and observability tools such as Great Expectations, Deequ, Soda, Monte Carlo, Bigeye, or Talend Data Quality.
  • Familiarity with data warehouses and lakehouse technologies: Snowflake, BigQuery, Redshift, Databricks, or Azure Synapse.
  • Experience with ETL/ELT orchestration tools (Airflow, Prefect, dbt) and embedding checks into DAGs and CI/CD workflows.
  • Knowledge of data cataloging and governance platforms (Collibra, Alation, Informatica EDC) and metadata management best practices.
  • Strong understanding of data modeling concepts (star/snowflake schemas), schema evolution, and implications on quality checks.
  • Competence with data lineage, impact analysis tools, and techniques for traceability across pipelines.
  • Ability to create monitoring dashboards and visualizations using BI tools (Tableau, Power BI, Looker) and observability platforms (Grafana, Prometheus).
  • Experience with cloud platforms (AWS, GCP, Azure) including common data services (S3, GCS, Redshift, BigQuery, Snowflake).
  • Familiarity with version control (Git), unit testing frameworks, and CI/CD pipelines for automated deployment of data quality code.
  • Basic knowledge of data privacy, security, and regulatory compliance (GDPR, HIPAA, SOC2) and techniques for PII detection and masking.
  • Experience in record linkage, deduplication, and master data management (MDM) techniques.
  • Proficiency in writing clear, reproducible SQL and code-based tests and implementing regression testing for data transformations.

Soft Skills

  • Strong problem-solving and root cause analysis skills with attention to detail and a forensic approach to data issues.
  • Excellent verbal and written communication to explain complex technical findings to non-technical stakeholders and executives.
  • Stakeholder management and negotiation skills to build consensus with product owners, data engineers, and business groups.
  • Project management and prioritization capabilities to manage cross-functional remediation programs and multiple concurrent incidents.
  • Curiosity and a continuous improvement mindset to proactively identify opportunities for automation and process enhancement.
  • Ability to work in agile teams and adapt to fast-paced, evolving data platform ecosystems.
  • Coaching and mentoring aptitude to uplift data literacy and quality ownership across the organization.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Bachelor’s + 2–5 years relevant experience, or Master’s degree in Data Science, Business Analytics, Computer Science, or related field.

Relevant Fields of Study:

  • Computer Science
  • Data Science / Analytics
  • Information Systems
  • Statistics / Mathematics
  • Business Intelligence / Management Information Systems

Experience Requirements

Typical Experience Range:

  • 2–5 years for mid-level Data Quality Analyst roles; 5+ years for senior or lead positions.

Preferred:

  • Prior experience in a data quality, data governance, analytics engineering, or data engineering role in enterprise environments.
  • Demonstrated track record implementing automated data quality frameworks and delivering measurable improvements in data reliability.
  • Exposure to regulated industries (finance, healthcare, fintech, insurance) or large-scale cloud data platforms is a plus.
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