Abhishek Shukla

Senior Data Engineer @Quantiphi

Gurugram, HR, IN
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WORK HISTORY

Apr 2023 — Present

Senior Data Engineer @Quantiphi

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Gurugram, IN

Reduced ticketing latency 6 h → <30 min with Kinesis → Redshift streaming stack.Tuned Redshift queries to <30 s via partitioning, sort keys & physical report tables.Built Python-powered Gen AI reporting web app, freeing analysts 2 days/week.Consolidated two marketplace schemas into a single star model, enabling one-click dashboards.

EDUCATION

2020 — 2020

Udacity

Data Engineering

2018 — 2021

University of Mumbai

Master of Computer Applications - MCA

2015 — 2018

Chatrapati Sahuji Maharaj Kanpur University, Kanpur

Bachelor of Computer Applications

ABOUT ABHISHEK SHUKLA

I’m a Senior Data Engineer who loves turning messy, high-velocity data into clear, business-ready insight. Over the last four years at Quantiphi I’ve built cloud-native platforms that power real-time ticket-sales dashboards, computer-vision analytics for the Bundesliga, and IVR performance reporting for a global bank. What I do:Design & own end-to-end data architectures on AWS—Kinesis, Glue, Redshift, EMR, Lambda, Step Functions.Blend streaming and batch paradigms with PySpark, SQL and Python to keep data fresh in minutes, not hours.Optimise warehousing and query performance (sub-30 s SLAs) and automate reports in QuickSight and natural-language LLM summaries.Champion data quality, cost efficiency and CI/CD (Git, Docker, CodePipeline). Impact so far:Cut report latency from 2 h → 5 min and saved analysts 40 % effort for a sports ticketing client.Unified schemas from two marketplaces into a single model adopted company-wide.Boosted IVR call containment 12 % via KPI-driven post-call analytics.Reduced manual video-tagging 60 % by productising CV model outputs for scouting teams. Tools & Certs: Python • SQL • PySpark • Apache Spark • Redshift • Glue • Kinesis • Airflow • Snowflake • QuickSight • Delta Lake • DBT • AWS-SAA • SnowPro Core.I thrive in agile teams where data engineering, product thinking and business value meet. If you’d like to swap ideas on real-time pipelines or making LLMs actually useful, let’s connect!

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