B Kumar Challakonda
Data Engineer | AWS • Azure • PySpark • SQL • Databricks • Airflow • Snowflake | Cut Forecasting Cycle by 30 Days | Migrated 200+ SAS Pipelines | Automated ML & UAT Workflows
- Role
- Data Engineer at Fannie Mae
- Location
- Carmel, IN, US
- LinkedIn followers
- 500 followers
About B Kumar Challakonda
Crafting data solutions that drive efficiency and innovation is my passion. With 3.5+ years of experience as a Data Engineer, I specialize in designing and modernizing scalable data pipelines across AWS and Azure environments. My expertise lies in leveraging PySpark, SQL, Databricks, Airflow, and Snowflake to build automated, reliable systems that enhance data quality and reduce manual effort.At Fannie Mae, I significantly improved the forecasting process by reducing a 30-day reporting cycle to mere hours through automation of UAT/grid validation workflows. This not only enhanced data consistency by 10% but also enabled analytics teams to efficiently process tens of millions of loan-level records by migrating over 200 SAS pipelines into PySpark cutting runtime from 12 hours to just 3.I am adept at building scalable ETL/ELT systems using AWS (EMR, S3, Glue) and Azure (Data Factory), supporting both analytics and ML teams with high-reliability pipelines. My technical skills include Terraform for infrastructure as code, Kafka for streaming pipelines, and CI/CD practices for continuous integration.Open to roles such as Data Engineer or Analytics Engineer where I can further apply my skills in AWS, Azure, PySpark, SQL, Databricks, Snowflake, Airflow, and more. Let\'s connect if you\'re looking for someone who can transform your data infrastructure into a powerhouse of insights!
Experience
Data Engineer
Sep 2022 — Present · Reston, VA, US
Forecasting Application Modernization (FAME)~ Reduced forecasting cycle by 30 days by building automated ETL pipelines using AWS S3, EMR, Step Functions, Lambda, and SQL-driven quality checks.~ Built YAML-based PySpark transformations improving ML dataset reliability by 15% on AWS EMR.~ Automated UAT grid validation (15-minute runtime), eliminating hours of manual QA for monthly cycles.~ Integrated Azure Data Factory + Databricks to deliver 12 standardized business-user grids backed by Aurora Postgres and lookup APIs.~ Improved forecasting data consistency by 10% through validation rules, metadata management, and reconciliation.Single Family Revenue Capital (SFR Capital)~ Migrated 200+ SAS pipelines to PySpark, improving performance (12 hrs to 3 hrs) on Glue + EMR.~ Designed SQL-driven ETL pipelines for tens of millions of loan records using S3, Redshift, and Snowflake.~ Restored SLA compliance from 2 days to 4 hours using Step Functions + Lambda orchestration.~ Increased pipeline reliability by 20% YoY with monitoring, validation, and automated alerting.~ Delivered governed datasets + SQL dashboards reducing reconciliation time by 24 hours per cycle.
Education
George Mason University
Master's degree, Data Modeling/Warehousing and Database Administration
Amrita School of Engineering
Bachelor's degree, Mechanical Engineering
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