- Analyze and validate data for accuracy, completeness, and integrity.

- Develop and implement data quality standards, rules, and processes.

- Identify, assess, Perform data profiling and resolve data quality issues across systems.

- Monitor and report on data quality metrics and KPIs.

- Collaborate with data engineers, database developers, and business stakeholders.

- Support data governance initiatives and ensure compliance with data policies.

- Conduct root cause analysis of data issues.

- Document procedures and maintain accurate records of data quality activities.

- Validate data extraction, transformation, and loading from source to target systems.

- Perform data profiling, data validation, and reconciliation.

- Review data models, mappings, and architecture documents to derive test scenarios.

Required Qualifications :

- Bachelors degree in Computer Science, Statistics, Information Management, or related field.

- 5 to 7 years of experience in data testing, data analysis or data quality roles.

- Proficiency in SQL and data manipulation tools.

- Familiarity with data quality tools and frameworks.

- Excellent analytical, problem-solving, and communication skills.

Preferred Skills :

- Strong SQL skills and experience with ETL tools (e.g., Informatica, Talend, SSIS).

- Familiarity with data warehousing concepts (e.g., star/snowflake schemas, dimensions, facts).

- Experience with test management tools (e.g., JIRA,Test Rail).

- Experience with data visualization tools (e.g., Power BI).

- Excellent analytical and problem-solving skills.

- Experience working in cross-functional teams and agile environments.

Nice To Have :

- Cloud experience (Microsoft Azure, AWS, Snowflake).

- Data Governance experience.

Required Technical/ Functional Competencies :

Domain/ Industry Knowledge : :

- Basic knowledge of customer's business processes relevant technology platform or product.

- Able to prepare process maps, workflows, business cases and simple business models in line with customer requirements with assistance from SME and apply industry standards/ practices in implementation with guidance from experienced team members.

Requirement Gathering And Analysis :

- Working knowledge of requirement management processes and requirement analysis processes, tools and methodologies.

- Able to analyse the impact of change requested / enhancement / defect fix and identify dependencies or interrelationships among requirements and transition requirements for the engagement.

Product/ Technology Knowledge :

- Working knowledge of technology product/platform standards and specifications.

- Able to implement code or configure/customize products and provide inputs in design and architecture adhering to industry standards/ practices in implementation.

- Analyse various frameworks/tools, review the code and provide feedback on improvement opportunities.

Architecture Tools And Frameworks :

- Basic knowledge of architecture Industry tools & frameworks.

- Able to analyse available tools and frameworks for review by the SME and plan for tool configurations and development.

Architecture Concepts And Principles :

- Basic knowledge of architectural elements, SDLC, methodologies.

- Able to apply various architectural constructs in the projects and identify various architectural patterns and implement.

Analytics Solution Design :

- High-level awareness of a wide range of core data science/analytics techniques, their advantages, disadvantages, and areas of application.

Tools & Platform Knowledge :

- Familiar with wide range of mainstream commercial and open-source data science/analytics software tools, their constraints, advantages, disadvantages, and areas of application.