Snehal Patil
Data Quality Engineer · 4 years experience
Nashik, Maharashtra
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Professional Summary
- Dedicated Data Quality Engineer with 4 years of experience in ensuring data integrity and reliability across ETL pipelines and data warehouses
- Proficient in SQL, Python, and data validation frameworks for comprehensive data quality assessments
- Skilled in designing and implementing automated data quality checks and monitoring systems
- Experienced in ETL testing, data profiling, and reconciliation processes for complex data flows
- Knowledgeable in data governance, compliance standards, and regulatory requirements for data management
- Familiar with big data technologies, cloud platforms, and data pipeline orchestration tools
- Strong analytical skills for identifying data anomalies, inconsistencies, and quality issues
- Collaborative professional working with data engineers and analysts to establish data quality standards
Project Experience
Customer Analytics Data Pipeline Validation
Designed comprehensive data quality checks for customer analytics pipelines, implementing automated validation rules and reconciliation processes. Created dashboards for real-time data quality monitoring, ensuring 99.9% data accuracy for business reporting. Technologies: SQL, Python, Pandas, Airflow, Tableau, Great Expectations.
E-commerce Data Warehouse ETL Testing
Led ETL testing efforts for a large e-commerce data warehouse, validating data transformations, schema changes, and data loading processes. Implemented data profiling and anomaly detection, reducing data quality issues by 70%. Technologies: SQL, Apache Spark, AWS, dbt, Power BI, Deequ.
Financial Services Data Compliance Framework
Developed data quality and compliance validation framework for financial data pipelines, ensuring adherence to regulatory standards. Created automated audit trails and data lineage tracking for compliance reporting. Technologies: Python, Snowflake, Azure, Kafka, NumPy, Compliance Tools.
LLM Training Data and RAG Quality Validation
Designed comprehensive data quality pipeline for an LLM fine-tuning platform, validating training corpus for label consistency, bias detection, PII scrubbing, and class imbalance. Implemented RAG embedding quality assessment using cosine similarity drift metrics and chunking strategy optimization. Set up data contracts enforcing schema and statistical constraints across ML feature stores. Technologies: LLM Training Data Validation, RAG Data Quality, Data Contracts, Feature Store Testing, AI Anomaly Detection, Python, Great Expectations, dbt, Snowflake.
Work Experience
Data Quality Engineer
[Company name] · [Start date – End date]
4 years of hands-on experience as a Data Quality Engineer at data-driven companies in Nashik. Developed and maintained data quality frameworks that improved data accuracy by 95% and reduced ETL failures by 60%.
Responsibilities
- Design and implement data quality validation rules and automated testing frameworks
- Perform ETL testing, data profiling, and reconciliation for data pipelines and warehouses
- Collaborate with data engineers to ensure data integrity throughout the ETL process
- Develop and maintain data quality dashboards and monitoring systems
- Conduct data governance assessments and ensure compliance with data standards
- Identify and resolve data quality issues, inconsistencies, and anomalies
- Create and execute data validation scripts using SQL and Python
- Work with business analysts to understand data requirements and quality expectations
- Implement data quality metrics and KPIs for continuous monitoring
- Participate in data modeling and schema design reviews for quality considerations
- Automate data quality checks and integrate them into CI/CD pipelines
- Provide training and documentation on data quality best practices
- Validate LLM training data quality, test ML pipeline data integrity, enforce data contracts, monitor embedding quality for RAG systems, and detect data drift in deployed AI models