qa engineer (auto) for data platforms

EY provides assurance, consulting, tax, strategy, and transactions services across more than 150 countries and territories. Its teams use data, AI, and advanced technology to help clients address business challenges, create value, and build trust in capital markets.

• Design and implement data quality validation frameworks across data platforms and pipelines;
• Validate ETL/ELT processes, including data transformations and business rules;
• Perform source-to-target data validation and reconciliation across systems;
• Define and execute automated data quality checks for completeness, accuracy, consistency, and timeliness;
• Identify and investigate missing, duplicate, and inconsistent data;
• Collaborate with data engineers, analysts, and business stakeholders to define validation rules and acceptance criteria;
• Support testing of data pipelines and data integration processes across modern data architectures;
• Perform root cause analysis for data issues and support remediation efforts;
• Contribute to data quality standards, governance, and best practices across engagements;
• Document validation logic, test scenarios, and data quality processes.

• Bachelor’s and/or Master’s degree in Computer Science, Engineering, Mathematics, or a related field;
• 2–5 Years of experience in data engineering, data QA, or data testing;
• Strong proficiency in SQL;
• Experience testing ETL/ELT data pipelines and transformation logic;
• Good understanding of data modelling concepts, including fact/dimension models and SCDs;
• Familiarity with data warehousing and modern data architectures;
• Experience working with large datasets and validating data at scale;
• Exposure to Azure, AWS, or GCP;
• Strong analytical thinking and problem-solving skills;
• Ability to work with structured and unstructured data;
• Effective communication and stakeholder management skills;
• Nice to have: experience with data quality tools or frameworks such as Great Expectations, dbt tests, or Deequ; Python programming for validation and automation; data pipeline orchestration tools such as Airflow or Azure Data Factory; CI/CD practices for data platforms; data governance and metadata management tools such as Purview, Collibra, or Informatica; Financial Services experience; agile or lean development methodologies.

• 13Th salary;
• Provident Fund;
• Private Medical and Life Insurance;
• Flexible work schedule;
• Friday afternoon off;
• EY Tech MBA and EY MSc in Business Analytics;
• EY Badges digital learning certificates;
• Mobility programs for working abroad;
• Paid Sick Leave;
• Paid Paternity Leave;
• Yearly wellbeing days off;
• Maternity, Wedding and New Baby Gifts;
• EY Employee Assistance Program with counselling, legal, and financial consultation services.