**Role & seniority: ** 6–9 years Experienced ETL Testing & QA Engineer for ETL pipelines/data platforms/cloud data engineering solutions (Deloitte Technology & Transformation).

**Stack/tools: **

Testing/automation: Python, PyTest, PySpark, Robot Framework, TestNG
SQL: Advanced SQL for data validation
Platforms: Databricks, Snowflake, Redshift, BigQuery, Synapse
ETL tools: Informatica, Ab Initio, Talend, DataStage, dbt (any)
Cloud: AWS/Azure/GCP (hands-on with at least one)
CI/CD: Jenkins, Azure DevOps, GitHub Actions, GitLab
Data formats: Parquet, Avro, JSON, CSV, XML
Bonus/test tools: Great Expectations, Deequ, Soda, Monte Carlo
Top 3 responsibilities:
1. Design/implement automated test frameworks for ETL/ELT pipelines and reusable validation components (e.g., row counts, checksums, null checks, referential integrity).
2. Validate end-to-end data quality (source-to-target reconciliation, transformation logic, business rules; completeness/accuracy/timeliness/integrity).
3. Run and troubleshoot tests (regression/integration/system testing; analyze failures/root cause; support fixes; validate incremental loads/CDC/migrations; integrate with CI/CD).
Must-have skills:
- ETL concepts + data warehousing/data lake testing (including UNIX familiarity)
- Automation with Python/PyTest (and PySpark)
- Advanced SQL for reconciliation and validation
- Experience with at least one cl

Full Description

The team

Deloitte’s Technology & Transformation practice can help you uncover and unlock the value buried deep inside vast amounts of data. Our global network provides strategic guidance and implementation services to help companies manage data from disparate sources and convert it into accurate, actionable information that can support fact-driven decision-making and generate an insight-driven advantage. Our practice addresses the continuum of opportunities in business intelligence & visualization, data management, performance management and next-generation analytics and technologies, including big data, cloud, cognitive and machine learning.

Your work profile 6-9 years experienced ETL Testing and quality assurance engineer for ETL pipelines, data platforms, and cloud-based data engineering solutions. Drive test strategy, automation, data validation, and stakeholder engagement across data transformation initiatives.

Key Responsibilities Design and implement automated test frameworks for ETL / ELT pipelines. Validate source-to-target data reconciliation, transformation logic, and business rules. Develop automated test scripts for data completeness, accuracy, timeliness, and integrity checks. Perform database, file, and API level testing across structured and semi-structured data. Create reusable automation components for row counts, checksum validation, null checks, and referential integrity. Execute regression, integration, and system testing for data pipelines. Analyze test failures, identify root causes, and work closely with data engineers to resolve issues. Validate incremental loads, CDC logic, and historical data migrations. Integrate automation tests with CI/CD pipelines. Prepare test plans, test cases, defect reports, and execution summaries.

Technical Skills

ETL / Data Testing: Strong understanding of ETL concepts, data warehousing, Data lakes and UNIX.

Automation: Python / PyTest / PySpark/ Robot Framework / TestNG

SQL: Advanced SQL for complex data validation

Data Platforms: Databricks, Snowflake, Redshift, BigQuery, Synapse

ETL Tools: Informatica, Ab Initio, Talend, DataStage, dbt (any)

Cloud: AWS / Azure / GCP (hands-on experience with at least one)

CI/CD: Jenkins, Azure DevOps, GitHub Actions, GitLab

Data Formats: Parquet, Avro, JSON, CSV, XML

Bonus: Great Expectations, Deequ, Soda, Monte Carlo

Education Bachelor’s degree in computer science, Information Technology, or related field.

ETL Test Engineer

Full Description