Upendra Dixit

DevOps Engineer

Bangalore Urban, Karnataka, India5 yrs 10 mos experience

Key Highlights

Led data platform transformation at Zoomcar.
Reduced customer query resolution time by 50%.
Achieved 25% cloud cost reduction through optimization.

Stackforce AI infers this person is a Data Engineering and AI Solutions expert in the SaaS industry.

Contact

Skills

Core Skills

Data EngineeringCloud Data ArchitecturesAi SolutionsData Science

Other Skills

PythonSQLSparkKafkaAirflowBigQueryGenerative AIData AnalyticsCloud MigrationApache AirflowGraph DBRandom ForestSHAPPower BISpacy

About

Lead Data Engineer with 6+ years of experience building highly scalable, fault-tolerant, distributed data platforms processing multi-terabyte pipelines daily. Expert in Python, SQL, Spark, Kafka, Airflow, and BigQuery, with strong experience in cloud data architectures on GCP/AWS. Proven track record designing optimized data models, building resilient batch + streaming systems, and driving performance engineering and low-level debugging on large production clusters. AI-literate engineer with hands-on experience deploying LLM-powered systems and enabling end-to-end MLOps.

Experience

5 yrs 10 mos

Total Experience

Average Tenure

Current Experience

Zoomcar

3 roles

Lead Data Engineer

Promoted

Apr 2025 – Present · 1 yr 2 mos

𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 & 𝗗𝗮𝘁𝗮 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 – Lead the Data Engineering team at Zoomcar, defining and executing the technology vision for the company’s data platform. Drove enterprise-wide modernization, aligning platform capabilities with business goals and championing scalable innovation in analytics and AI. Built and mentored a high-performing engineering team from the ground up, improving delivery velocity by 40% and reducing data-related incidents by 35%. Partnered with C-suite executives to prioritize infrastructure investments and deliver measurable business impact.
𝗔𝗜 & 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 – Designed, developed, and deployed a Generative AI chatbot leveraging RAG architecture, reducing customer query resolution time by 50%. Built a Gen AI–powered voice analytics solution for customer support calls, 𝐞𝐧𝐚𝐛𝐥𝐢𝐧𝐠 80% 𝐟𝐚𝐬𝐭𝐞𝐫 𝐢𝐧𝐬𝐢𝐠𝐡𝐭 𝐞𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐢𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐦𝐚𝐤𝐢𝐧𝐠 𝐬𝐩𝐞𝐞𝐝 𝐛𝐲 3×. Deployed 𝐋𝐚𝐧𝐠𝐟𝐮𝐬𝐞 𝐟𝐫𝐨𝐦 𝐨𝐩𝐞𝐧 𝐬𝐨𝐮𝐫𝐜𝐞 𝐟𝐨𝐫 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠, 𝐥𝐨𝐠𝐠𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐆𝐞𝐧 𝐀𝐈 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬, ensuring quality and reliability in AI outputs.
𝗖𝗹𝗼𝘂𝗱 & 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 – 𝐒𝐩𝐞𝐚𝐫𝐡𝐞𝐚𝐝𝐞𝐝 𝐭𝐡𝐞 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐦𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐟𝐫𝐨𝐦 𝐀𝐖𝐒 𝐭𝐨 𝐆𝐂𝐏, delivering the project with zero major disruptions. Achieved 25% cloud cost reduction through architecture optimization and rightsizing. Deployed and manage Apache Airflow and Rudderstack from open source—with Rudderstack serving as a robust event streaming and analytics platform for capturing in-app events and user behavior data— achieving 99.9% uptime for orchestration and event streaming pipelines.

PythonSQLSparkKafkaAirflowBigQuery+4

Senior Data Engineer

Apr 2024 – May 2025 · 1 yr 1 mo

Data Scientist

Dec 2021 – Apr 2024 · 2 yrs 4 mos

Data ScienceGraph DB

O9 solutions, inc.

Data Scientist

Aug 2021 – Dec 2021 · 4 mos · Bangalore Urban, Karnataka, India

𝗗𝗲𝗺𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴
𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁, 𝗢𝗽𝘁𝘂𝗻𝗮, 𝗦𝗛𝗔𝗣
Developed a forecasting framework for a retail client to predict sales of different product category - 𝘂𝘀𝗲𝗱 𝗖𝗿𝗼𝘀𝘁𝗼𝗻 𝗺𝗲𝘁𝗵𝗼𝗱 𝗳𝗼𝗿 𝗶𝗻𝘁𝗲𝗿𝗺𝗶𝘁𝘁𝗲𝗻𝘁 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 𝗮𝗻𝗱 𝗳𝗼𝗿 𝗿𝗲𝘀𝘁 𝗱𝗲𝗽𝗹𝗼𝘆𝗲𝗱 𝗮 𝗿𝗮𝗻𝗱𝗼𝗺 𝗳𝗼𝗿𝗲𝘀𝘁 𝗺𝗼𝗱𝗲𝗹 𝗵𝘆𝗽𝗲𝗿-𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝘁𝘂𝗻𝗲𝗱 𝘄𝗶𝘁𝗵 𝗢𝗣𝗧𝗨𝗡𝗔
Constructed multiple features to model drastic changes in demand due to COVID-19
Build a SHAP plot to communicate impact of different features to the prediction of the model

PythonRandom ForestSHAPData Science

Pgp glass pvt. ltd.

Data Scientist

Aug 2020 – Aug 2021 · 1 yr · Vadodara, Gujarat, India

𝗙𝘂𝗿𝗻𝗮𝗰𝗲 𝗙𝘂𝗲𝗹 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻
𝙏𝙚𝙘𝙝 𝙎𝙩𝙖𝙘𝙠: 𝙅𝙪𝙥𝙮𝙩𝙚𝙧, 𝙋𝙮𝙩𝙝𝙤𝙣, 𝙍𝙖𝙣𝙙𝙤𝙢 𝙁𝙤𝙧𝙚𝙨𝙩, 𝙎𝙃𝘼𝙋
Built a Random Forest Regression model to predict the hourly fuel consumption and created a Power BI dashboard showing fuel flow to be run for next hour and trend of fuel consumed and used SHAP plot to communicate impact of different features to the prediction of the model
Found ideal Reversal Time of the furnace by performing EDA(studying first and second derivative of temperature of furnace with time) on historical data
𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗥𝗮𝗻𝗸𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺
𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗦𝗽𝗮𝗰𝘆, 𝗡𝗘𝗥, 𝗛𝘂𝗴𝗴𝗶𝗻𝗴𝗳𝗮𝗰𝗲, 𝗿𝗲𝗴𝗲𝘅
Developed a system to rank the CV’s according to their similarity matching with the job description using libraries like spacy, re, huggingface and models like NER to extract name, mobile number, email, education and companies worked at from CV
Scored CV based on the skills provided by the hiring manager (using cosine similarity)

PythonRandom ForestPower BISHAPData Science