Akhil Reddy Edla

Senior Data Engineer with 5+ years designing and operating scalable batch and streaming data pipelines across GCP, AWS, and Azure cloud platforms. Proven expertise in SQL optimization, Python automation, and orchestration frameworks (Cloud Composer, Airflow) that directly transfer to Azure Data Factory and cloud data warehouse architecture. Delivered 70% reduction in manual data refresh work through CDC automation and 40% query performance gains via schema optimization.

Brings production-grade data quality monitoring, governance practices, and cross-functional collaboration skills to support analytics and reporting at scale.

Senior AI/ML Data Engineer - CenterPoint Energy - Houston, TX

(2023-04)

• Designed and deployed scalable batch and near-real-time data pipelines ingesting 150+ Snowflake tables into BigQuery via Cloud Run-based Python framework with watermark CDC, schema drift detection, and automated reconciliation-reducing manual refresh work ~70% while maintaining data integrity across daily loads.
• Optimized BigQuery database architecture and query performance ~40% through dbt Core incremental models, SCD Type 2 transformations, and strategic partitioning/clustering on high-cardinality columns, enabling sub-second response times for executive and operational reporting dashboards.
• Implemented and monitored comprehensive data quality checks integrating Monte Carlo APIs with custom Python validation logic to detect schema changes, freshness violations, and volume anomalies in real time-reducing incident resolution time ~35% and enabling proactive alerting across 150+ tables.
• Engineered near-real-time streaming pipelines using Pub/Sub, Datastream, and Cloud Functions with standardized error handling, dead-letter queues, and idempotent load patterns to support sub-minute latency operational datasets feeding analytics and reporting platforms.

Data Engineer - SAP India - Bengaluru, India

(2021-01 - 2022-12)

• Designed and maintained scalable batch ETL pipelines extracting large-volume datasets from SAP ECC/S4HANA (FI, MM, SD modules) and Oracle EBS into downstream analytics platforms, implementing incremental delta-load strategies and watermark-based CDC to optimize throughput for high-cardinality tables.
• Developed complex SQL transformations-multi-table joins, window functions, SCD Type 2 modeling, aggregations-to produce analytics-ready datasets for finance, procurement, and supply-chain reporting, ensuring referential integrity and business rule consistency across enterprise systems.
• Built training datasets for demand and revenue forecasting ML models by engineering time-series features (lag windows, rolling aggregates, seasonality indicators) from SAP finance and supply-chain data, delivering clean, labeled, model-ready inputs that enabled reproducible retraining cycles.
• Automated Python-based feature engineering and dataset refresh pipelines supporting ML forecasting workloads, applying null handling, outlier treatment, normalization, and train/validation split logic to ensure data quality and reproducibility across model iterations.

Master of Science - Computer Science - University of Central Missouri (2023-01 - 2024-05)