Rajesh Lakumarapu

About Rajesh Lakumarapu

Experienced Data Engineering Manager with over 15 years of expertise in designing, building, and optimizing large-scale data infrastructures. Proven track record in leveraging AWS services to deliver scalable and efficient big data solutions that drive business insights and support strategic decision-making. Adept at leading cross-functional teams, managing complex projects, and implementing best practices in data architecture and governance.Technologies worked on AWS Services : Redshift, S3, Athena, Airflow, Lambda, EMR, Glue, QuickSight, CloudWatch, IAM, KMS, SES• Big Data : Apache Spark, Sqoop, Hive, HBase• Scripting languages : SQL, Spark, Scala, Python• Microsoft BI stack : SQL Server, SSIS, SSRS, SSAS• Internal Amazon tools for data ingestion, orchestration, data exchange, compiling, deployment, scheduling, versioning, code-review• Visualisation tools : Tableau, Power-BI• Machine Learning : Supervised algoritms, Unsupervised algorithms, NLP• Versioning tools : GIT, TFSDomain experience Corporate Finance• US HealthCare (Medicaid,Non-Medicaid,SNAP,TANF)Experience in Big Data Engineering• Data Engineering Management• Extract, Transform and Load (ETL)• Data Conversion & Migration• Data Warehousing• Business Intelligence• Data Modeling• Data Visualisation• Data Science

Experience

1. Manager Iii, Big Data Engineering (Amazon Corporate Finance)

   Amazon

   Jul 2022 — Present · Bengaluru, IN

   Leading and managing a team of Big Data Engineers in GFT(Global FP & A Technology), Corp-FP & A team of Amazon, where we work with finance related information of different Amazon organisations like AWS, Retail, Advertising, Digital(Prime Video, Twitch, Music), Devices(Kindle, Alexa), Audible, Ring, etc. pulled from OFA/Essbase/HFM/TM1, and generate a S3-DataLake (consisting of redistributed & transformed numbers) using Spark/Scala/EMR, for all our internal customers as well as for financial reporting purposes through cubes in Cognos-TM1 financial system, where these numbers are used to determine the organizations\' P&L (Profit & Loss).AWS services : S3, EMR, Athena, Glue, Airflow, Redshift, Lambda, DynamoDB, CloudWatch, etc.Tech Stack : Scala, Spark, Python, SQL, GIT, etc.Internal Amazon tools/applications are used for data exchange, compiling, deployment, scheduling, versioning, code-review.

Education

• GreyAtom

  Data Science Masters Program, Python for Data Science, Machine Learning & Text Data Analysis.

  2019 — 2020

• The Kelkar Education Trusts Vinayak Ganesh Vaze College of Arts Science and Commerce Mithagar Road Mulund East Mumbai 400 081

  H.S.C., Science

  2004 — 2006

• KJ Somaiya College of Engineering, Vidyavihar

  Bachelor's degree, Electronics and Telecommunication

  2006 — 2010

• Manjunatha Vidyalaya

  S.S.C., Mathematics

  1992 — 2004

Skills

• Business Intelligence
• Analysis Services
• Sql
• Sql Server Integration Services (Ssis)
• Extract, Transform, Load (Etl)
• Rpa
• Tableau
• Ssrs
• Uipath
• Etl
• Pl/Sql
• Data Warehousing
• Sql Server Reporting Services (Ssrs)
• Data Visualization
• Ssis
• Microsoft Sql Server
• R Data Analytics