Dinesh Kumar

Results-driven Data Engineer with 2+ years of experience designing and delivering scalable cloud-native data pipelines, ETL workflows, and data warehousing solutions. Proficient in Python, PySpark, and SQL for large-scale data processing and transformation across structured and semi-structured datasets. Experienced in leveraging AWS services including S3, Glue, EMR, and Redshift to build reliable and efficient data infrastructure.

Skilled in Snowflake for modern cloud data warehousing and Apache Airflow for robust workflow orchestration and batch processing automation. Passionate about applying data modeling, performance optimization, and data governance principles to deliver production-ready, analytics-ready data products that drive informed business decisions across data-intensive environments.

Data Engineer at Ascensus (2024-06 – Present)

Client: Futureplan by Ascensus Global Services Domain: Investment Banking

• Designed and developed reusable cloud-native ETL pipelines on AWS to ingest structured and semistructured customer data from multiple source systems into an S3-based data lake, applying modular batch processing patterns and data modeling principles to support scalable and repeatable data onboarding workflows
• Built end-to-end extract, transform, and load processes using AWS Glue, PySpark, and SQL to cleanse, standardize, and enrich raw customer datasets before loading into Amazon Redshift data warehousing layers, ensuring data was consistently structured and consumption-ready for downstream reporting and analytics teams
• Implemented automated data quality validation checks and performance optimization techniques including partitioning, columnar storage, and indexing strategies within the pipeline to enforce completeness and schema compliance rules, supporting effective data governance and compliance policies across the platform
• Orchestrated automated data flow and workflow orchestration using Apache Airflow DAGs to schedule and manage batch processing execution across ingestion, transformation, and load stages, reducing manual intervention and ensuring reliable and timely delivery of customer data to business consumers
• Migrated customer data from legacy data warehouse sources to a modern AWS cloud architecture by applying data modeling, schema mapping, and incremental load strategies, enabling a governed, performance-optimized, and scalable data foundation aligned with enterprise data compliance and data warehousing standards.

Master of Business Administration in Business Administration – Hindustan College (2023)

Bachelor of Business Administration in Business Administration – The American College (2021)