Akhil Krishna A

AWS-focused Data Engineer with 3+ years of experience designing and optimizing ETL/ELT pipelines, cloud-native data lakes, and analytics platforms using Python, PySpark, SQL, AWS Glue, Amazon S3, Amazon Redshift, and Apache Airflow. Proven track record of improving pipeline reliability to 99.5% uptime, reducing data processing time by 55%, and delivering curated, analytics-ready datasets at enterprise scale. Hands-on experience with streaming architectures (Kafka), query optimization, data quality validation, and batch automation.

Data Engineer at Cognizant Technology Solutions (2023-01 – 2026-04)

• Designed and maintained production-grade ETL/ELT pipelines using Python, PySpark, AWS Glue, and Apache Airflow for structured data processing across enterprise analytics workloads.
• Built and optimized end-to-end data workflows for ingestion, cleansing, transformation, and loading into Amazon S3 (data lake) and Amazon Redshift (data warehouse), supporting 50+ downstream analysts.
• Achieved 99.5% pipeline uptime across production pipelines by implementing Apache Airflow monitoring, automated failure alerts, retry logic, and SLA-based scheduling controls.
• Reduced Amazon Redshift query execution time by 20% (avg. 45s to 36s) through distribution key optimization, sort key tuning, vacuum/analyze scheduling, and query rewriting.
• Automated batch scoring and data processing workflows using Python, cutting manual pipeline intervention by 60% and enabling engineering team to focus on higher-value development.
• Implemented data quality checks and validation frameworks across ingestion and transformation layers to improve consistency, completeness, and integrity of enterprise datasets.
• Delivered clean, query-ready datasets into Amazon Redshift and Amazon S3 for business intelligence dashboards, ML feature stores, and executive reporting.
• Collaborated with data scientists, analysts, and business stakeholders to translate requirements into reliable, scalable data transformation logic and pipeline architecture.
• Contributed to data modeling and warehousing design (star schema, SCD Type 2) for downstream analytics and reporting use cases.
• Architected and maintained cloud infrastructure across AWS Glue, S3, Lambda, and Redshift with orchestration through Apache Airflow DAGs for dependency management and scheduling.

B.Sc. in Computer Science – NGM College of Arts & Science (2019-03 – 2022-05)