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Key Responsibilities and Required Skills for ETL Tester

💰 $ - $

🎯 Role Definition

An ETL Tester is responsible for validating, verifying and assuring the quality of data as it moves through Extract, Transform, Load (ETL) pipelines and data migration projects. The role focuses on designing and executing comprehensive test cases for data integration, data warehousing, and Big Data platforms to ensure data accuracy, integrity, performance and compliance. The ideal ETL Tester combines deep SQL skills, hands-on experience with ETL tools (Informatica, SSIS, Talend), knowledge of data warehousing concepts, and a strong testing mindset including automation, regression and performance testing.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior QA Engineer with SQL and database testing experience
  • Data Analyst or BI Analyst transitioning into test engineering
  • Junior ETL Developer or ETL Support Engineer

Advancement To:

  • Senior ETL Tester / Lead ETL Tester
  • Data QA Lead or Test Manager (Data)
  • ETL Developer / Data Engineer or BI QA Architect

Lateral Moves:

  • BI Developer / Report Developer
  • Test Automation Engineer focusing on data pipelines
  • Data Governance or Data Quality Analyst

Core Responsibilities

Primary Functions

  • Design, develop and maintain detailed ETL test plans, test cases and data validation scripts to verify inbound and outbound data for batch and streaming ETL processes across source systems, staging and target data warehouses.
  • Execute functional data validation testing to confirm correctness of transformations, mappings, aggregations and business rules implemented in Informatica, SSIS, Talend, DataStage or custom ETL code.
  • Develop complex SQL queries, stored procedures and reconciliation scripts to compare source-to-target data, validate row counts, data types, null handling and referential integrity in OLTP and OLAP environments.
  • Create and maintain automated ETL test suites using test automation frameworks (Selenium not required for ETL, but frameworks like PyTest, DBUnit, Jenkins-driven scripts, or custom Python/SQL automation) to accelerate regression testing and CI/CD pipeline validation.
  • Perform end-to-end integration testing for data pipelines that include multiple upstream sources, transformations and downstream consumers (reports, dashboards, data lakes).
  • Execute data migration testing for system upgrades and cloud migrations (e.g., on-prem to AWS Redshift, Azure Synapse, GCP BigQuery) to ensure zero data loss and parity across environments.
  • Conduct performance and volume testing of ETL jobs, monitor job runtimes, resource utilization and recommend tuning strategies (partitioning, parallelism, pushdown optimization) to meet SLA requirements.
  • Validate data quality using profiling, rules-based checks and data quality frameworks; identify root causes, raise defects and work with ETL developers to remediate issues.
  • Create robust test data sets and synthetic data in compliance with data privacy rules (masking, obfuscation) to support realistic test scenarios across environments.
  • Implement and maintain test automation within CI/CD pipelines (Jenkins, Azure DevOps, GitLab CI), including automated validation after deployments and scheduled regression runs.
  • Perform schema validation, metadata reconciliation and ensure schema evolution is tested when fields are added, removed or retyped in the ETL process.
  • Validate incremental load logic, CDC (Change Data Capture) flows and ensure delta processing correctness, ordering, and idempotency.
  • Generate and maintain detailed defect reports, data validation reports and ETL test evidence; lead defect triage with development and data engineering teams.
  • Collaborate with data modelers and architects to ensure test coverage for star/snowflake schemas, slowly changing dimensions (SCD Type 1/2), surrogate keys and historical data requirements.
  • Test API-based data ingestion and validation for pipeline components that consume or expose REST/JSON or Kafka topics; validate message formats, schemas and offsets.
  • Work with BI teams to validate report data and dashboards (Power BI, Tableau, Looker) against canonical data sources and ETL outputs to ensure business-aligned metrics.
  • Create and maintain data lineage documentation and test artifacts that prove data traceability from source to target for compliance and audit requirements.
  • Participate in sprint grooming and planning to scope ETL testing tasks, estimate testing effort and define acceptance criteria for user stories involving ETL changes.
  • Design negative and boundary test cases for ETL transformations (data truncation, invalid data types, timezone and locale edge cases) and verify proper error handling and logging.
  • Coordinate cross-environment test runs (DEV, QA, UAT, PROD validation) and manage environment-specific data sets and connection profiles to ensure reproducible results.
  • Keep up-to-date with data engineering and testing best practices for Big Data technologies (Spark, Hadoop, Hive) and apply them to ETL test strategy where applicable.
  • Mentor junior testers in ETL testing methodology, SQL best practices and test automation patterns to raise team capability.

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 developing test metrics, KPIs and dashboards that measure ETL quality, failure rates and defect trends over time.
  • Work with DevOps to standardize test environments, seed data processes and database refresh strategies that accelerate test cycles.
  • Support compliance audits by delivering validated evidence of ETL testing and change records for regulated datasets.
  • Provide input on requirements and acceptance criteria for new ETL features and data products to ensure testability and observability.

Required Skills & Competencies

Hard Skills (Technical)

  • Expert SQL (ANSI SQL, window functions, joins, CTEs) for complex source-to-target validations and performance troubleshooting.
  • Hands-on experience with ETL tools: Informatica PowerCenter, Microsoft SSIS, Talend, IBM DataStage, or similar enterprise ETL platforms.
  • Experience with data warehouse concepts: star/snowflake schemas, dimensional modeling, slowly changing dimensions (SCD), surrogate keys.
  • Familiarity with Big Data technologies and distributed processing: Apache Spark, Hadoop, Hive, and testing strategies for large-scale datasets.
  • Test automation skills for ETL: Python, PyTest, shell scripting, DBUnit, or custom SQL-driven automation integrated into CI/CD (Jenkins, Azure DevOps).
  • Proficiency testing incremental loads, CDC processes and validating idempotency and ordering in streaming or micro-batch pipelines.
  • Experience with cloud data platforms: AWS Redshift, Snowflake, Azure Synapse Analytics, Google BigQuery, including testing data migrations to cloud.
  • Strong knowledge of data quality tools and frameworks, data profiling and rule-based validations; experience with tools like Great Expectations is a plus.
  • Performance and volume testing skills for ETL jobs; ability to interpret job logs, tuning ETL mappings and optimizing SQL performance.
  • Familiarity with source systems (ERP, CRM, flat files, APIs) and file formats (CSV, JSON, Parquet, Avro) commonly used in ETL processes.
  • Understanding of CI/CD for data pipelines, version control (Git), and orchestration tools (Airflow, Control-M) for validating scheduled jobs.
  • Knowledge of data governance, masking and PII handling practices to ensure test data privacy and compliance.

Soft Skills

  • Strong analytical mindset with attention to detail and ability to reason about data discrepancies and root cause analysis.
  • Excellent communication and stakeholder management skills to liaise between data engineering, QA, BI and business owners.
  • Organized, process-driven approach to test documentation, defect tracking and repeatable validation procedures.
  • Problem-solving orientation and proactive ownership of issues until remediation and regression verification.
  • Ability to work in Agile teams, adapt to changing priorities and estimate testing effort effectively.
  • Mentoring and knowledge-sharing attitude to upskill peers and improve testing standards across the team.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, Statistics or related field; or equivalent practical experience.

Preferred Education:

  • Bachelor's or Master's degree in Data Engineering, Computer Science, Information Technology, or related discipline.
  • Certifications in ETL tools (Informatica ProCertified), cloud data platforms (AWS/GCP/Azure) or test automation frameworks are a plus.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range: 2–7+ years in ETL testing, data validation or data QA roles.

Preferred:

  • 3+ years of dedicated ETL testing experience with strong SQL and one or more ETL tools.
  • Experience validating data migrations to cloud data warehouses and familiarity with Big Data testing concepts.
  • Exposure to test automation, CI/CD and performance/volume testing in data pipeline contexts.
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