Data Validation Specialist - Snowflake (4-5 yrs)

Primary responsibility will be to ensure the accuracy, integrity, and consistency of data within the organization's databases and systems. You will work closely with cross-functional teams to implement data validation processes and procedures, ensuring that data entered into the system meets predefined standards and requirements.

Responsibilities :

- Develop and implement data validation rules and procedures to ensure the accuracy and integrity of data between two different environments.

- Collaborate with developers to integrate data validation checks into the back-end systems and processes.

- Design and execute automated tests to validate data quality and identify anomalies or errors.

- Analyze data validation results and troubleshoot issues to determine root causes and implement corrective actions.

- Develop and maintain documentation related to data validation processes, including validation rules, test cases, and procedures.

- Work with cross-functional teams to define data quality standards and requirements.

- Monitor data quality metrics and trends and provide recommendations for continuous improvement.

Qualifications :

- Bachelor degree in computer science, information technology, or a related field.

- Proven experience in back-end data validation, data quality assurance, or a similar role.

- Snowflake experience with strong understanding of database concepts, SQL, data modeling, and ETL processes.

- Should have experience with ETL informatica Power Center, DataStage, shell scripting.

- Ability to analyze complex data sets and troubleshoot data-related issues.

- Experience with data validation tools and techniques, such as regular expressions, data profiling, and data cleansing.

- Proficiency in scripting or programming languages (e.g., Python, Java) for automation.

- Excellent analytical and problem-solving skills, with a keen attention to detail.

- Strong communication and collaboration skills, with the ability to work effectively in a team environment.

- Experience with Agile development methodologies is a plus.

- Familiarity with reporting platforms and tools, and data visualization tools is a strong plus.

- Data Validation : Perform data validation checks to ensure the accuracy, consistency, and quality of data in Snowflake.

- ETL Process Monitoring : Collaborate with the data engineering team to monitor and validate data ETL (Extract, Transform, Load) processes, ensuring proper data migration and transformation.

- Data Quality Assurance : Implement data validation rules and processes to ensure data quality standards are met, including completeness, accuracy, and timeliness.

- Troubleshooting and Issue Resolution : Investigate and troubleshoot data issues in Snowflake, identify root causes, and collaborate with development teams to implement solutions.

- Documentation and Reporting : Create and maintain documentation for data validation processes, standards, and test results. Provide regular reports to stakeholders regarding data quality metrics.

- Collaboration with Stakeholders : Work with business analysts, developers, and data scientists to understand data requirements and provide insights into data validation needs.

- Performance Optimization : Suggest improvements to enhance the performance and efficiency of data validation processes within Snowflake.

- Automation : Develop and implement automated test scripts and validation routines for large datasets.

- Compliance and Security : Ensure data validation processes comply with industry regulations and best practices for data security and privacy.

Required Skills and Qualifications :

Experience with Snowflake : Strong experience with Snowflake Data Warehouse, including data modeling, data integration, and query optimization.

SQL Expertise : Proficient in writing complex SQL queries for data validation, transformation, and troubleshooting.

Data Quality Management : Experience with data quality frameworks and methodologies, such as data profiling, data cleansing, and data matching.

ETL Process Knowledge : Familiarity with ETL tools (e.g., Talend, Informatica, Apache Nifi, etc.) and data pipeline development.

Problem-Solving : Strong analytical and troubleshooting skills to identify and resolve data issues efficiently.

Attention to Detail : Ability to spot discrepancies, inconsistencies, and inaccuracies in large datasets.

Communication Skills : Strong written and verbal communication skills to document processes, collaborate with teams, and report on data quality issues.

Programming Skills : Knowledge of programming languages such as Python or Java is a plus.

Familiarity with Data Warehousing Concepts : Understanding of data warehousing principles, data modeling, and OLAP concepts.