Tarun Chhabra
San Francisco,CA
Summary
Experienced Data Engineer and Tech Lead with a proven track record across three reputable firms - Amazon, Meta, and Goldman Sachs. Successfully adapted to diverse organizational cultures and delivered high-impact data engineering solutions. Strong foundation in both Data Engineering and Software Engineering, familiar with the entire tech stack from backend systems to cloud platforms. Recognized for understanding technical and cultural nuances, leading teams to build scalable, high-performance data systems aligned with business objectives. Mentors and trains future leaders, fostering a culture of accountability and continuous improvement. Expertise in cloud-based data architectures, real-time data processing using AWS services, and ETL pipeline optimization. Focuses on delivering solutions that meet technical and business needs while ensuring data accuracy, governance, and security.
Overview
8
8
years of professional experience
Work History
Sr. Software Engineer
Amazon Web Services (AWS)
San Francisco, CA
10.2022 - Current
- Automated invoicing and billing workflows , architecting event-driven microservices using AWS (S3, Glue, Lambda, DynamoDB) to eliminate processing delays, transition to real-time data sources, and increase free cash flow by ~$1B as part of Amazon S-Team leadership goals.
- Led a team of 3 engineers to develop an interactive billing system , integrating DynamoDB and AWS Gateway APIs , enabling real-time discount recalculations and billing artifact generation, improving billing accuracy by 200 bps and contributing to $150M in free cash flow impact .
- Designed and deployed a centralized audit configuration and execution platform leveraging AWS Lambda, Redshift, and Step Functions , automating data validation and reducing manual effort by 50 hours per month for financial analysts, accelerating billing artifact processing.
- Developed and integrated APIs for key stakeholders (Late Fee Invoicing, Payments, and third-party portals), establishing a single source of truth for billing artifacts and enabling automated late fee enforcement, enhancing revenue collection and cash flow timing .
- Optimized billing artifact processing , reducing the P90 population timeline from Day 12 to Day 2 , significantly improving invoice turnaround time and accelerating revenue recognition.
Data Engineer
Meta
Menlo Park, CA
04.2021 - 10.2022
- Redesigned the revenue data framework , reducing dataset update latency from 3+ days to real-time (hourly) , ensuring leadership (CMO, Meta) had immediate access to revenue insights for strategic decision-making and ad supply-demand optimization.
- Developed an Ads Delivery Execution Framework , partnering with ML Engineers to integrate advanced ranking models , enabling the launch of Lead Generation via business messaging and forms , improving ad interaction rates by prioritizing users most likely to engage .
- Built Delivery Funnel Analytics , tracking user interactions across ad surfaces to identify engagement drop-offs, and developed Growth Analytics to provide churn insights for advertisers , enabling targeted upsell opportunities and increased ad spend .
Quantitative Strategist, Vice President
Goldman Sachs
Dallas, TX
07.2017 - 04.2021
- Developed multi-layered data services , implementing a hot and cold storage system where traders accessed data quickly through MemSQL (30-day retention), while regulatory and downstream systems used the same API to access data in the data-lake for slower processing, improving data retrieval efficiency and supporting real-time trading decisions.
- Created and optimized End-of-Day (EOD) risk computation tools for the Equities Derivatives desk , ensuring timely and accurate risk assessments , reducing processing time by 6 hours per day and ensuring regulatory compliance across models.
- Led a team of 3 engineers in the EOD Risk Team , overseeing recruitment and training, building the team with one member promoted to Associate and expanding the team to 5 members by the end of summer to meet growing global demands in EOD risk analysis.
Education
Master of Science - Statistics
University of Illinois at Urbana-Champaign
Urbana-Champaign, Illinois05.2017
Master of Science - Civil Engineering (Risk Management)
University of Illinois at Urbana-Champaign
Urbana-Champaign, Illinois05.2016
Bachelor of Engineering - Civil Engineering
Birla Institute of Technology And Science (BITS)
Pilani07.2013
Skills
- Backend Engineering
- Data Engineering
- ETL development
- Python
- Java
- Presto
- Spark
- Airflow
- AWS Services(Lambda, DynamoDB, Glue, S3, API Gateway, RedShift)
- SQL
- Object-oriented programming
- Technical architecture
Timeline
Sr. Software Engineer
Amazon Web Services (AWS)
10.2022 - Current
Data Engineer
Meta
04.2021 - 10.2022
Quantitative Strategist, Vice President
Goldman Sachs
07.2017 - 04.2021
Master of Science - Civil Engineering (Risk Management)
University of Illinois at Urbana-Champaign
Master of Science - Statistics
University of Illinois at Urbana-Champaign
Bachelor of Engineering - Civil Engineering
Birla Institute of Technology And Science (BITS)
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