Headline

Data Engineer @ PayPal | Building Scalable Financial Data Pipelines & ETL Systems | Python, SQL, Airflow, PySpark | AWS | Big Data & Analytics | ML & Quant Systems

About

I build data systems that people actually depend on to make decisions. I started my career working on ML systems at NVIDIA, where I deployed models on edge devices and worked on ETL pipelines and latency-sensitive systems. That experience made one thing clear early on even the best models don’t matter if the data pipelines behind them aren’t reliable. That’s what pulled me into data engineering. At PayPal, I work on large-scale financial data pipelines and ETL systems that power analytics for Finance, Risk, and Product teams. I’ve built end-to-end pipelines processing 10M+ records/day from raw ingestion into a Dropzone data lake to PySpark transformations and curated datasets in Oracle, orchestrated using Apache Airflow. These data pipelines and ETL workflows support dashboards and internal applications used by 10K+ users across multiple business units. My core focus is on building reliable, scalable, and production-grade data systems using Python, SQL, PySpark, Airflow, and AWS with strong emphasis on data quality, pipeline reliability, and performance optimization. Outside of work, I explore quantitative finance building trading strategies, backtesting signals, and working with time-series models to understand market behavior. Right now, I’m interested in working on high-performance data platforms, financial data systems, and distributed data pipelines where data engineering and modeling intersect. Tech Stack & Specializations: Languages & Tools: Python, SQL, PySpark, Apache Airflow, Tableau, TensorFlow, Keras, Scikit-learn, DataBricks, DBT Cloud & Big Data: AWS (Lambda, S3, Athena, Glue),ETL, Data Modelling, Data Warehouse (Redshift, Bigquery),NVIDIA Jetson, Edge AI, DeepStream SDK, ML & Quant: Time-Series Modeling, Signal Generation, Backtesting, LSTM, ARMA-GARCH, Statistical Validation Concepts: Equity Financial Data, Execution Analytics, Model Explainability, Risk Modeling, Volatility Forecasting

Location

Hyderabad

AI stack

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Work experience

Data engineer 2 at PayPal, Mar 2023 - Present · 3 yrs 6 mos, Bengaluru, Karnataka, India, • Built and operated large-scale financial data pipelines (10M+ records/day) using Python, SQL and Pyspark enabling analytics for Finance, Risk, and Product teams fully orchestrated by Apache Airflow. Downstream output: 20+ Tableau dashboards and 3 internal web apps serving Finance, Risk, Product Analytics, and Compliance teams. • Migrated financial data pipelines from Oracle to BigQuery, redesigning ETL workflows for scalable cloud-native analytics and supporting projected cost savings (~$350K annually). • Automated 8 complex financial-metric ETL workflows end-to-end, driving a 25% increase in pipeline throughput and eliminating approximately 100 engineering hours of manual effort per quarter. • Restructured 12 critical Airflow DAGs with parallelism tuning and resource-level optimisation achieving 15% efficiency improvement and zero SLA breaches on executive-reporting pipelines over a continuous 6-month period. • Built a data quality validation framework with schema drift detection, automated alerting, and pipeline monitoring ensuring financial data freshness SLAs for all offline reporting workloads across 4 business units. • Architected a SQL analytics platform ingesting employee stock-holding declarations and financial KPI data to surface high-attrition risk signals; findings adopted by Finance and Compliance leadership to implement targeted retention strategies. • Enhanced data security by implementing robust SQL-level permissioning and power controls, reducing vulnerabilities by 30% and ensuring compliance. • Upgraded compensation intelligence pipelines to improve internal forecasting accuracy, leading to better compensation planning and headcount budgeting.

Machine Learning Solution Engineer - Edge AI (Nvidia) at RP tech India, Aug 2021 - Sep 2022 · 1 yr 2 mos, Bengaluru, Karnataka, India, • Led end-to-end development of a secure data pipeline for Amazon’s Kiosk Project, integrating encryption keys and Amazon S3 to achieve a 95% reduction in unauthorized data access, enabling secure data transfer from Jetson edge modules to AWS for real-time trend analysis and quantitative decision-making. • Architected and deployed machine learning and deep learning models on Nvidia Jetson devices (Nano, Xavier), optimizing inference with TensorRT and quantization-aware training, reducing latency by 40% for high-frequency edge AI applications. • Enhanced model performance by 50% through advanced pruning, feature engineering, and hyperparameter tuning, enabling efficient training and deployment of deep learning models using TAO for real-time inferencing. • Designed and optimized data modeling and preprocessing pipelines for cross-functional teams, streamlining data preparation and boosting model training efficiency by 40% through custom ETL processes and framework recommendations. • Developed and delivered Product Requirements Documents (PRDs) to solve complex business challenges, driving a 40% improvement in client performance by designing optimized data extraction strategies and recommending ML frameworks. • Built and deployed a YoloV5-based Driver Safety Detection POC for a manufacturing client, achieving 89% accuracy and completing the project 3 weeks ahead of schedule, leveraging Nvidia’s Inference Server for scalable model deployment.

Data Analyst at MedTourEasy, Oct 2020 - Nov 2020 · 2 mos, Karnataka, India, • Worked with different Visualization-tools like Tableau and Power-BI, and worked on the practical implementation of the Time-Series model using Facebook-Prophet. • Implemented a project of Predicting Blood Donation using the Automated Machine Learning Tool TPOT Classifier and achieved an AUC score of 0.76.

Education

Master of Technology - MTech, Computer science and Engineering specialization in data science, Birla Institute of Technology and Science, Pilani

Bachelor of Technology - BTech, Computer science of engineering specialization in data science, Jain (Deemed-to-be University)