Professional Overview
Data Engineer with 5+ years of experience in SQL, Python, PySpark, ETL/ELT, Databricks, Delta Lake, Azure Data Factory, Snowflake, MySQL, and Oracle. Skilled in building scalable data pipelines, data transformation, validation, reconciliation, data quality, performance optimization, and cloud data solutions.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built scalable ETL pipelines using Python, PySpark, SQL and Databricks.
- Developed complex SQL queries for data processing and validation.
- Performed data quality checks and source-to-target reconciliation.
- Optimized SQL and PySpark workloads for better performance.
- Monitored pipelines and resolved data processing issues.
Key Achievements / KPIs
- Improved data quality through automated validation checks.
- Reduced manual effort through SQL-based validation automation.
- Improved pipeline stability through root-cause analysis.
- Ensured accurate and consistent enterprise data.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built scalable ETL pipelines using Python, PySpark, SQL and Databricks.
- Developed complex SQL queries for data processing and validation.
- Performed data quality checks and source-to-target reconciliation.
- Optimized SQL and PySpark workloads for better performance.
- Monitored pipelines and resolved data processing issues.
Key Achievements / KPIs
- Improved data quality through automated validation checks.
- Reduced manual effort through SQL-based validation automation.
- Improved pipeline stability through root-cause analysis.
- Ensured accurate and consistent enterprise data.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built scalable ETL pipelines using Python, PySpark, SQL and Databricks.
- Developed complex SQL queries for data processing and validation.
- Performed data quality checks and source-to-target reconciliation.
- Optimized SQL and PySpark workloads for better performance.
- Monitored pipelines and resolved data processing issues.
Key Achievements / KPIs
- Improved data quality through automated validation checks.
- Reduced manual effort through SQL-based validation automation.
- Improved pipeline stability through root-cause analysis.
- Ensured accurate and consistent enterprise data.
Education
Field of Study: Computer Science
Areas of Expertise
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
