Data Engineer

Engineered scalable ETL pipelines using Python, SQL, AWS Glue, and Amazon S3 to ingest, cleanse, and transform user engagement and transaction data, reducing manual data processing effort by 45% and improving data availability for analytics.
Optimized complex SQL queries using CTEs, window functions, indexing, partitioning, and execution plan analysis, improving query performance by 40% across 10M+ transactional records while supporting business-critical reporting.
Implemented batch data processing pipelines with Apache Kafka and Python to process user activity and application event data, increasing pipeline reliability by 35% and ensuring consistent downstream data delivery.
Designed dimensional data models and automated data quality validation frameworks for customer, gameplay, and payment datasets, improving reporting accuracy by 30% and enabling reliable KPI tracking across product and business teams.
Developed interactive Tableau dashboards and automated reporting solutions by integrating curated datasets from SQL data warehouses, reducing report generation time by 50% and enabling data-driven product, marketing, and operational decisions.