Quality Engineering Lead (Test Lead) at Accenture

Mumbai, Maharashtra, India

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, Data and AIIndustries

Requirements

Minimum 5 years of experience (7+ years preferred) in ETL or data testing with strong understanding of data engineering principles
Must have skills: Data Warehouse ETL Testing
Hands-on experience with Databricks, Python, and PySpark
Experience in test automation using Python/PySpark and integrating with CI/CD pipelines (e.g., Azure DevOps, Jenkins, GitHub Actions)
Strong expertise in data validation, data reconciliation, and testing large-scale data pipelines
Experience testing data warehouse aggregation layers and reporting outputs (e.g., Power BI, Tableau)
Solid understanding of metadata-driven frameworks and data quality validation concepts (accuracy, completeness, consistency, validity, timeliness, uniqueness)
Familiarity with Agentic AI or AI-driven data quality automation tools
15 years full-time education

Responsibilities

Validate ETL/ELT processes developed in Databricks (Python/PySpark) for correctness, performance, and scalability
Conduct data validation, reconciliation, and regression testing across staging, curated, and aggregated layers
Ensure consistency of data transformations between the data lake, warehouse, and reporting layers
Perform schema validation, business rule verification, and data lineage testing
Validate data aggregation logic and ensure data accuracy in analytical and reporting layers
Cross-check BI reports/dashboards (e.g., Power BI, Tableau) against source and transformed data
Test data models, measures, and KPIs to ensure reporting accuracy
Execute tests for data quality dimensions (accuracy, completeness, consistency, validity, timeliness, uniqueness)
Validate metadata-driven frameworks that dynamically manage ETL logic and schema evolution
Test AI-driven or Agentic AI data quality frameworks—validating automation, intelligent rule generation, anomaly detection, and self-healing data flows
Develop and maintain automated data testing frameworks using Python, PySpark, or Databricks notebooks
Integrate automated tests into CI/CD pipelines
Implement reusable test scripts for data validation, metadata verification, and data quality monitoring
Contribute to the test strategy, test planning, and execution for continuous integration and testing
Work closely with data engineers, data scientists, BI developers, and AI solution architects to ensure data accuracy and reliability
Create and maintain test plans, test cases, test data, and defect logs
Support root cause analysis and provide actionable insights to improve data quality and testing efficiency

Skills

Data Warehouse ETL Testing

Databricks

Python

PySpark

ETL Testing

ELT Testing

Data Validation

Data Reconciliation

Regression Testing

Schema Validation

Data Lineage

Data Pipeline Testing

Metadata Validation

Accenture

Global professional services for digital transformation

About Accenture

Accenture provides a wide range of professional services, including strategy and consulting, technology, and operations, to help organizations improve their performance. Their services assist clients in navigating digital transformation, enhancing operational efficiency, and achieving sustainable growth. Accenture's offerings include cloud migration, cybersecurity, artificial intelligence, and data analytics, which are tailored to meet the needs of various industries such as financial services, healthcare, and retail. What sets Accenture apart from its competitors is its extensive industry knowledge and ability to deliver comprehensive solutions that address both immediate challenges and long-term goals. The company's aim is to support clients in reducing their environmental impact while driving innovation and growth.

Dublin, IrelandHeadquarters

1989Year Founded

$8.5MTotal Funding

IPOCompany Stage

Consulting, Enterprise Software, CybersecurityIndustries

10,001+Employees

Risks

Rapid AI advancements may outpace Accenture's current capabilities, risking competitive disadvantages.

Integration challenges from multiple acquisitions could affect Accenture's operational efficiency.

The rise of AI-driven startups may disrupt Accenture's market share in customer service solutions.

Differentiation

Accenture's acquisitions enhance its capabilities in digital twin technology for financial services.

The company is expanding its expertise in net-zero infrastructure through strategic acquisitions.

Accenture's focus on software-defined vehicles positions it as a leader in automotive innovation.

Upsides

Accenture's investment in EMTECH supports central bank modernization amid digital currency evolution.

The acquisition of Award Solutions boosts Accenture's presence in the growing 5G and IoT markets.

Accenture's strategic acquisitions align with high-growth markets like digital twins and net-zero projects.

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