Job Description & Summary: A career within…. A career within Data and Analytics services will provide you with the opportunity to help organisations uncover enterprise insights and drive business results using smarter data analytics. We focus on a collection of organisational technology capabilities, including business intelligence, data management, and data assurance that help our clients drive innovation, growth, and change within their organisations in order to keep up with the changing nature of customers and technology. We make impactful decisions by mixing mind and machine to leverage data, understand and navigate risk, and help our clients gain a competitive edge.  

Responsibilities: 

Senior Data Quality Engineer  

As a Senior Data Quality Engineer with 8–10 years of experience, you will lead end-to-end quality engineering for enterprise data applications, data platforms, data warehouses, and data lakes.  

You will work closely with business stakeholders, product owners, developers, and data engineering teams to understand requirements, define quality standards, design effective test coverage, validate data pipelines, and support reliable releases in Agile delivery environments.  

Lead testing activities across planning, test design, execution, defect tracking, release validation, and quality reporting.  

Perform manual, functional, system, integration, regression, UI, API, and end-to-end testing for enterprise and data applications.  

Validate source-to-target data, transformations, business rules, completeness, accuracy, and reconciliation across data pipelines.  

Test data processing built with Apache Spark and validate input and output data from streaming or queue-based integrations such as Kafka and Kinesis.  

Create reusable test scenarios, test cases, test data, and regression suites to improve coverage and delivery efficiency.  

Use Postman for API validation and tools such as JIRA or Spira for defect lifecycle management, traceability, and reporting.  

Participate in requirement reviews and functional design discussions, perform test impact analysis, and define acceptance and quality criteria.  

Collaborate with clients, product owners, developers, and cross-functional teams to prevent defects and resolve issues within release timelines.  

Support production releases by validating functional and non-functional requirements and providing evidence for release approvals.  

Work effectively in Agile teams, contribute to ceremonies and sprint quality updates, and deliver testing within iterative release cycles.  

Guide junior team members, review testing outputs, share knowledge, and promote consistent quality engineering practices.  

Apply working knowledge of SQL and relational databases such as MySQL to perform data validation and investigation.  

Understand data lake and data warehouse concepts, including storage, security, cataloging, architecture patterns, data modelling, and star schemas.  

Have practical exposure to cloud data platforms on AWS or Azure; familiarity with services such as S3, Redshift, Glue, Athena, EMR, DMS, Airflow, or equivalent services is advantageous.  

Demonstrate basic programming knowledge in Python or PySpark and collaborate effectively with data engineering teams on technical troubleshooting.  

Communicate clearly with technical and business stakeholders, manage multiple work tracks, and demonstrate strong ownership, interpersonal skills, and continuous learning.  

Exposure to test automation, reporting tools such as Power BI or QuickSight, emerging technologies, Generative AI, or relevant data and quality certifications is an added advantage.  

Mandatory skill sets: 

Testing, Test Design, Defect Tracking,  

Preferred skill sets: 

Python, Pyspark 

Years of experience required: 

 8-10yrs 

Education qualification: 

B.Tech / M.Tech / M.E / MCA/B.E