Data Engineer - Tester at NTT DATA, Inc. in Bengaluru, IN-KA, India

Job description

• The Data Engineer - Tester at NTT DATA in Bengaluru will be responsible for building and maintaining automated test suites to validate ETL/ELT pipelines using PySpark and DataTester. The role requires hands-on experience with data testing frameworks, cloud storage (AWS S3), and advanced SQL skills. Key duties include ensuring data quality, conducting regression and component testing, and collaborating with data engineers to implement test-driven development. Candidates must have strong proficiency in PySpark, Apache Spark, AWS S3, and Git for CI/CD pipelines. The position is suited for professionals with a solid background in data testing, big data processing, and database querying. Collaboration and defect tracking are essential aspects of the role.
• NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.
• We are currently seeking a Data Engineer - Tester to join our team in Bengaluru, Karnātaka (IN-KA), India (IN).

Responsibilities

• Pipeline Validation: Build automated test suites to validate complex ETL/ELT pipelines using PySpark and DataTester.
• Data Quality Assurance: Verify data accuracy, completeness, and consistency across source systems and AWS S3 data lakes.
• Test Automation: Design and maintain scalable, reusable data testing frameworks to perform schema validation, null checks, and data reconciliation.
• Component Testing: Conduct rigorous unit, integration, and regression testing on distributed data processing applications.
• Defect Tracking: Identify, document, and track data anomalies, performance bottlenecks, and pipeline failures.
• Collaboration: Partner closely with core Data Engineers and Platform teams to understand data models and implement test-driven development (TDD) practices.

Requirements

• Data Testing Frameworks: Hands-on experience with DataTester to automate data quality checks.
• Big Data Processing: Strong proficiency in PySpark and Apache Spark for manipulating and querying large-scale datasets.
• Cloud Storage: Deep understanding of AWS S3 storage structures, partitioning strategies, and file formats (Parquet, Delta, Avro).
• Testing Methodology: Solid background in functional and non-functional data testing, including data migration and regression testing.
• Database & Querying: Advanced SQL skills for querying relational databases and verifying data warehousing solutions.
• Version Control: Experience with Git and CI/CD deployment pipelines for automated test execution

Skills

Required

• DataTester
• PySpark
• Apache Spark
• AWS S3
• Parquet, Delta, Avro file formats
• Advanced SQL
• Git
• CI/CD pipelines