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Key Responsibilities and Required Skills for Data Quality Assurance Analyst

💰 $ - $

DataAnalyticsQuality AssuranceITData Governance

🎯 Role Definition

The Data Quality Assurance Analyst is responsible for ensuring the reliability, accuracy, and usability of enterprise data across ingestion, transformation and reporting layers. This role owns the definition and execution of data quality rules, automated validation tests, reconciliation procedures, data profiling and issue remediation workflows in partnership with Data Engineering, Analytics, Product, and Business stakeholders. The ideal candidate blends strong technical testing skills (SQL, Python, ETL testing frameworks) with pragmatic data governance knowledge to reduce data incidents and support data-driven decisions.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst transitioning to QA for datasets and pipelines.
  • QA / Test Engineer with experience in backend or data systems.
  • ETL Developer or Junior Data Engineer moving into quality assurance.

Advancement To:

  • Senior Data Quality Analyst / Lead
  • Data Quality Manager or Data Governance Manager
  • Data Engineering Manager or Head of Data Operations
  • Analytics Engineering Lead (dbt/CI-CD focused roles)

Lateral Moves:

  • Data Engineer (ETL/ELT)
  • Business Intelligence / Analytics Engineer
  • Data Steward or Data Catalog Specialist

Core Responsibilities

Primary Functions

  • Develop, document, and operationalize a comprehensive data quality framework that includes rules, thresholds, SLA definitions, and remediation workflows to ensure data accuracy, completeness, consistency, timeliness, and uniqueness across batch and streaming pipelines.
  • Design and implement automated data validation and testing suites using SQL, Python, and specialized tools (e.g., Great Expectations, Deequ, dbt tests), integrating tests into CI/CD pipelines to prevent data regressions prior to production deployment.
  • Perform proactive data profiling and statistical analysis to identify anomalies, outliers, and patterns of data drift; translate profiling insights into measurable data quality rules and continuous monitoring checks.
  • Build and maintain reconciliation processes that validate source-to-target ETL/ELT transformations, row counts, aggregate checks and dimensional conformity across data lakes, warehouses and marts.
  • Create, maintain and enforce data contracts and service-level agreements (SLAs) with data producers and consumers, including schema expectations, freshness windows, and acceptable error budgets.
  • Implement and manage data quality monitoring dashboards and alerting in tools such as Looker, Tableau, Power BI, or native cloud monitoring to provide real-time visibility into data health and to drive operational response.
  • Lead root cause investigations for data incidents—document findings, identify upstream causes, propose and track remediation steps, and recommend preventive controls to eliminate recurrence.
  • Collaborate closely with Data Engineering to instrument pipelines with test hooks, lineage metadata, schema migration strategies, and to incorporate automated checks into Airflow, dbt, or other orchestration workflows.
  • Author and maintain reproducible test cases, test data sets, and regression test plans; ensure tests are version-controlled and integrated into the team’s CI/CD and deployment processes.
  • Validate complex transformations, business logic implementations and metric definitions used by analytics and BI teams—ensure parity between source systems and derived KPIs.
  • Perform schema and data type validation, enforce semantic consistency (master data alignment), and flag schema evolution risks that could break downstream consumers.
  • Implement privacy and compliance checks for PII/PHI data—ensure masking, redaction, and access controls are applied and validated according to regulatory requirements (GDPR, CCPA, HIPAA as applicable).
  • Operationalize data lineage capture and metadata documentation to support impact analysis, audits and traceability from source systems to dashboards and ML models.
  • Partner with Product and Business stakeholders to translate ambiguous business rules into executable validation criteria and acceptance tests that align with domain requirements.
  • Configure alert triage procedures and integrate data incidents with ticketing systems (Jira, ServiceNow) and incident response playbooks to ensure timely resolution and stakeholder communication.
  • Optimize and tune validation queries and test logic for performance at scale in cloud environments (Snowflake, BigQuery, Redshift) to minimize resource cost and runtime.
  • Develop sampling and statistical testing strategies for very large tables and streaming events to provide high-confidence validation when full scans are impractical.
  • Conduct regression testing for pipeline changes, schema migrations, or release rollouts and sign off on data readiness prior to production promotion.
  • Maintain and enrich the data catalog and quality metadata (badges, quality scorecards, known issues) to help data consumers assess dataset fitness for purpose.
  • Mentor junior QA/data analysts on best practices for data testing, profiling techniques, and tool usage; lead periodic training sessions on the data quality framework and expectations.
  • Evaluate, pilot, and recommend modern data quality tooling and automation (commercial or open-source) that align with the organization’s data strategy and scale needs.
  • Provide executive and stakeholder reporting on data quality KPIs, trend analysis, and the business impact of data quality improvements (e.g., reduced incident volumes, time saved downstream).
  • Contribute to change management by reviewing proposed schema changes, feature flags, and migrations to identify data quality risk and acceptance criteria before rollout.

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.
  • Draft and maintain runbooks, playbooks, and standard operating procedures for data quality triage and remediation.
  • Assist in vendor evaluations and procurement of data quality, cataloging, or observability platforms.
  • Facilitate cross-team workshops to align on data definitions, golden records, and measurement frameworks.
  • Aid in designing feature flags or canary deployments for new data sources to minimize production impact.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL proficiency for complex joins, window functions, aggregation testing, reconciliation and performance tuning in large datasets.
  • Strong Python (or Scala/Java) scripting for automated testing, data profiling, ETL validation, and integration with testing frameworks.
  • Hands-on experience with data testing and observability tools such as Great Expectations, Deequ, dbt tests, Soda SQL, Monte Carlo, Monte Carlo-style observability or commercial equivalents.
  • Practical knowledge of data orchestration and CI/CD tools (Airflow, dbt Cloud, GitHub Actions, Jenkins) to integrate data tests into deployment pipelines.
  • Familiarity with cloud data platforms and warehouses: Snowflake, BigQuery, Redshift, and cloud providers AWS, GCP, Azure.
  • Experience performing data profiling, statistical sampling, anomaly detection and distributional tests to design robust validation checks.
  • Understanding of data modeling concepts (star schema, normalized models), metadata management and data lineage capture.
  • Experience with ETL/ELT frameworks and tools (Informatica, Talend, Fivetran, Stitch, custom Spark/Beam jobs) and validating their outputs.
  • Proficiency with BI/reporting tools (Looker, Tableau, Power BI) to validate metrics and create quality dashboards for stakeholders.
  • Familiarity with data governance frameworks and tools (Collibra, Alation, data catalogs) and experience enforcing data stewardship processes.
  • Knowledge of privacy and security practices for data masking, encryption, role-based access control, and compliance checks.
  • Version control (Git), unit testing best practices, and test automation skills for maintaining reliable test suites.

Soft Skills

  • Excellent analytical thinking with a meticulous attention to detail when defining tests and interpreting results.
  • Strong verbal and written communication skills for translating technical findings into business-impacting narratives and remediation plans.
  • Stakeholder management and the ability to negotiate SLAs and data contracts across cross-functional teams.
  • Problem-solving mindset with persistence in root cause analysis and driving issues to closure.
  • Collaborative team player able to work inside Agile squads and cross-functional delivery teams.
  • Time management and prioritization skills to balance reactive incident response with proactive quality engineering.
  • Teaching and mentoring aptitude to grow data quality literacy across the organization.
  • Customer-focused attitude to ensure data consumers’ needs drive quality priorities.
  • Adaptability and continuous-learning orientation for evolving data stacks and tooling.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor’s degree in Computer Science, Information Systems, Data Science, Statistics, Mathematics, Engineering, or a related technical field.

Preferred Education:

  • Master’s degree in Data Science, Analytics, Computer Science, or MBA with strong quantitative coursework.
  • Professional certifications (Google Cloud Professional Data Engineer, SnowPro, DAMA CDMP, or vendor-specific data quality/certifications).

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range:

  • 3–6 years of hands-on experience in data quality, ETL testing, data engineering, or analytics engineering roles.

Preferred:

  • 5+ years in data quality or data testing roles with demonstrable projects implementing automated data validation, monitoring, and remediation in production environments.
  • Prior exposure to cloud-native data platforms (Snowflake/BigQuery/Redshift), data testing frameworks (Great Expectations, dbt, Deequ), and CI/CD integration.
  • Demonstrated experience working with business stakeholders to define data contracts and measure data fitness for decision making.
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