Niveditha Narasimhan
About Niveditha Narasimhan
Niveditha Narasimhan is a Technical Lead with expertise in ETL tools and data engineering. She has extensive experience in implementing projects using agile methodology and has worked with notable organizations across various business domains.
Work at Altimetrik
Currently, Niveditha Narasimhan serves as a Technical Lead at Altimetrik, a position she has held since 2019. In this role, she applies her extensive knowledge in ETL tools and cloud services to lead technical projects. Her responsibilities include overseeing the design and implementation of data engineering solutions, utilizing agile methodologies to enhance project efficiency.
Education and Expertise
Niveditha Narasimhan earned her Bachelor of Engineering (B.E.) in Computer Science and Engineering from KGiSL Institute of Technology and Engineering, completing her studies from 2009 to 2013. She possesses strong expertise in various ETL tools such as Pentaho, Informatica, SSIS, and BIS, along with proficiency in SQL and UNIX. Additionally, she has hands-on experience with AWS Cloud services, including Redshift, S3, Codebuild, Codepipeline, and EMR.
Background
Before joining Altimetrik, Niveditha worked at Mindtree as a Module Lead from 2018 to 2019 and at ABCO India Private Ltd as an ETL Developer from 2016 to 2018. Her professional experience spans various business domains, including Market Risk, Credit Risk, Investment Banking, and US Healthcare. She has collaborated with notable organizations such as the Federal Reserve Bank, Bank of New York Mellon, Ancestry.com LLC, The Advisory Board Company, and The American Red Cross.
Technical Skills and Projects
Niveditha Narasimhan is skilled in using Databricks, Python, Pyspark, Spark, and Hive for data engineering tasks. She has experience in tuning Pentaho jobs and transformations to address performance bottlenecks. Additionally, she is capable of automating Informatica jobs through parameterization and creating reusable transformations and mapplets, enhancing the efficiency of data processing workflows.