Stefan Gromoll
Sr. Performance Engineering Manager, Amazon Redshift at Amazon Web Services (AWS)
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolStefan Gromoll is a senior performance engineering leader focused on Amazon Redshift with eight years of experience driving performance and data-loading best practices at AWS. He progressed from senior performance engineer to Sr. Performance Engineering Manager, leading efforts to optimize query execution and reproducible benchmarking for Redshift. Stefan brings hands-on data engineering chops—contributing to the well-known awslabs/amazon-redshift-utils repo by improving TPC-DS/TPC-H loading scripts and enhancing the SimpleReplay tool for extracting and replaying Redshift query logs. His background spans startup founding and data platform leadership, combining product-minded technical management with deep systems and benchmarking expertise. Based in New York, he pairs a physics-trained analytical approach (MIT BS, PhD work in astrophysics) with practical experience improving large-scale analytics infrastructure.
8 years of coding experience
14 years of employment as a software developer
BS Physics, BS Physics at Massachusetts Institute of TechnologyPhD candidate Astrophysics, PhD candidate Astrophysics at Stony Brook University
Github Skills (10)
data-loading10
amazon-redshift10
sql10
etl-process10
data-warehouse10
datamart10
s3-bucket9
python9
amazon-s39
aws-s39
Programming languages (3)
Github contributions (5)
awslabs/amazon-redshift-utils
Dec 2020 - Jun 2022
Amazon Redshift Utils contains utilities, scripts and view which are useful in a Redshift environment
Role in this project:
Data Engineer
Data EngineerContributions:16 reviews, 19 commits, 22 PRs in 1 year 6 months
Contributions summary:Stefan primarily contributes to data loading and transformation processes, specifically related to the TPC-DS and TPC-H benchmarks within the Amazon Redshift environment. Their work includes updating data loading scripts, fixing data import parameters, and modifying COPY statements. They also focused on enhancing and maintaining the SimpleReplay tool, including adding filters and improving its functionality for extracting and replaying Redshift query logs. The user's work demonstrates a deep understanding of Redshift data loading best practices and the utilization of these utilities to reproduce query execution.
redshiftamazonutilsamazon-redshiftaws
intermix/collector
Dec 2017 - Dec 2017
Contributions:4 pushes, 2 branches in 4 days
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