Jan 2024 – Jun 2025 · 1 yr 5 mos · United States

• As a Python Data Engineer at Stripe, I led backend and data platform initiatives powering real-time payment processing, subscription analytics, fraud detection, and AI/ML workflows. I built scalable ETL/ELT pipelines to process millions of structured and unstructured financial records daily, while creating APIs and dashboards to deliver insights to product, finance, and analytics teams. My work ensured high throughput, data accuracy, and fault-tolerant, production-ready systems across multiple cloud environments.
• Developed Python ETL/ELT pipelines using Spark, Hadoop, and Celery to handle batch and streaming data from Stripe APIs, internal systems, and third-party platforms, supporting analytics and ML workflows.
• Built REST APIs with FastAPI, secured via OAuth2/JWT, deployed with AWS Lambda & API Gateway, enabling seamless access to processed payment and subscription data.
• Designed optimized data models in PostgreSQL, MySQL, MongoDB, and DynamoDB, balancing transactional and analytical workloads for high-performance queries.
• Developed React dashboards connected to backend services to visualize payment metrics, transaction trends, pipeline health, and AI/ML outputs for internal stakeholders.
• Containerized services with Docker, deployed on Kubernetes clusters with Helm, and implemented CI/CD pipelines via GitHub Actions and Jenkins for reliable production delivery.
• Implemented monitoring, observability, and anomaly detection with Grafana, Prometheus, ELK stack, and CloudWatch, improving system reliability and proactive issue resolution.
• Collaborated with data scientists, analysts, product managers, and engineers in Agile teams, delivering end-to-end production-ready solutions that enhanced fraud detection, subscription analytics, and operational efficiency.
• Automated repetitive tasks, data validation, and reporting workflows, reducing manual intervention and improving pipeline throughput.

Sep 2023 – Dec 2023 · 3 mos · United States

• As a Research Assistant at Stevens Institute of Technology, I developed Python-based data processing workflows and dashboards to support research experiments. I focused on automating data cleaning, transformation, and visualization for both structured and unstructured datasets, enabling reproducible, high-quality research outcomes.
• Built Python scripts and prototypes to automate data processing and experiment workflows, ensuring modularity and reproducibility.
• Designed and optimized ETL pipelines for structured and unstructured datasets, improving data quality and availability for research analysis.
• Developed interactive dashboards using Dash, allowing research teams to quickly visualize and interpret experimental results.
• Assisted in experiment implementation and testing, ensuring code reliability and proper documentation for reproducible outcomes.
• Collaborated with faculty, students, and research teams to integrate Python-based solutions into broader research workflows.
• Contributed to technical documentation, enabling future teams to replicate and extend experimental pipelines efficiently.

Nov 2021 – Dec 2022 · 1 yr 1 mo · India

• At Meesho, I designed and maintained scalable ETL pipelines and cloud-native workflows to process massive volumes of structured and unstructured data, supporting analytics, AI/ML initiatives, and business intelligence dashboards. I ensured reliability, observability, and efficiency across all data platforms while integrating diverse data sources for actionable insights.
• Built batch and streaming pipelines using Spark, Hadoop, Kafka, and Redis, integrating data with AWS Glue, Snowflake, Redshift, and Oracle for analytics-ready datasets.
• Developed and optimized data models in PostgreSQL, MySQL, and MongoDB to support analytics, ML workflows, and reporting.
• Automated ingestion and transformation pipelines with Airflow, containerized jobs with Docker, and orchestrated workloads using Kubernetes and Helm.
• Implemented CI/CD pipelines with GitHub Actions and Jenkins; automated infrastructure provisioning using Terraform, AWS CDK, Ansible, and CloudFormation following GitOps practices.
• Developed Python APIs and microservices to deliver processed data to dashboards, analytics tools, and ML models, enabling near real-time decision-making.
• Monitored pipeline and system health with Prometheus, Grafana, ELK, Datadog, and Splunk, ensuring end-to-end observability and reliability.
• Integrated multiple data sources including APIs, IoT devices, and internal systems, creating a centralized repository for analytics and predictive modeling.
• Optimized pipelines for memory, speed, and cost-efficiency, supporting large-scale processing of millions of records daily.
• Collaborated with data scientists, product managers, and DevOps teams in Agile workflows, delivering production-ready data solutions.

Dec 2020 – Oct 2021 · 10 mos

• At Xpertnest, I led AI/ML development projects, building predictive analytics systems, IoT integrations, and LLM-powered RAG pipelines. I designed end-to-end data pipelines, cloud-deployed models, and APIs to operationalize AI solutions across industries including smart cities, banking, and manufacturing.
• Developed and deployed AI/ML models using Python, TensorFlow, and PyTorch for predictive maintenance, anomaly detection, and customer behavior analysis.
• Built data pipelines feeding models, integrating with Spark, Hadoop, and Celery for asynchronous processing of structured, unstructured, and IoT data.
• Developed REST APIs (Flask & FastAPI) to expose ML model functionality to front-end and system integrations.
• Deployed ML models on AWS SageMaker and serverless components via AWS Lambda, ensuring scalability and cost-efficiency.
• Implemented LLM-driven RAG pipelines, embeddings, and fine-tuning for NLP, knowledge retrieval, and predictive analytics applications.
• Containerized AI services with Docker and orchestrated deployments using Kubernetes, ensuring robust production-grade model serving.
• Automated unit and integration testing for ML pipelines and APIs, integrating CI/CD workflows for reliability and reproducibility.
• Monitored model and system performance with observability tools, identified bottlenecks, and optimized pipelines for real-time applications.
• Collaborated with cross-functional Agile teams, contributing to product planning, sprint delivery, and mentoring engineers on AI/ML and DevOps practices.

May 2019 – Dec 2020 · 1 yr 7 mos · India

• At Zeta , I developed cloud-native banking applications and scalable ETL pipelines, enabling secure, high-performance digital banking operations. I contributed to backend, frontend, and data platforms, ensuring real-time processing, compliance, and observability for large-scale financial systems.
• Developed cloud-native banking apps (cards, loans, deposits) using Django, Node.js, React, and TypeScript with a microservices architecture.
• Built and integrated RESTful and GraphQL APIs, enabling seamless, low-latency digital banking experiences.
• Engineered core banking modules such as transaction processing, lending, and issuance, handling millions of operations daily on PostgreSQL.
• Designed ETL pipelines for high-volume transaction and fraud-detection data, supporting real-time monitoring and compliance reporting.
• Deployed microservices on AWS (EKS/ECS, Lambda, API Gateway), secured with KMS, and monitored with CloudWatch, Prometheus, and Grafana.
• Implemented OAuth2/JWT authentication, TLS encryption, PCI-DSS and GDPR compliance, and real-time risk monitoring.
• Containerized applications with Docker, orchestrated with Kubernetes & Helm, and streamlined CI/CD with DevOps teams.
• Built dashboards, observability, and monitoring systems to proactively detect anomalies in mission-critical environments.
• Collaborated in Agile teams, mentoring juniors, and contributing to innovation initiatives like loyalty systems and backend automation.