Aug 2025 – Oct 2025 · 2 mos · Bengaluru, Karnataka, India

• 🔹 Integrated Python scripts with Airflow DAGs for automated batch processing
• 🔹 Tuned Spark jobs by adjusting partitioning, caching, and broadcast joins
• 🔹 Performed large-scale data aggregation and feature engineering for ML pipelines using PySpark
• 🔹 Developed unit tests for PySpark jobs using Pytest & SparkSession

May 2024 – Aug 2025 · 1 yr 3 mos · Bengaluru, Karnataka, India

• 🔹 Designed and built ETL pipelines using Apache Airflow, Spark, and SQL
• 🔹 Worked extensively on data modeling and schema design for structured & semi-structured data
• 🔹 Developed real-time streaming pipelines using Kafka and PySpark
• 🔹 Optimized queries on Snowflake, BigQuery, and PostgreSQL to enhance performance
• 🔹 Deployed pipelines on cloud platforms like GCP and AWS (Glue, S3, Lambda)
• 🔹 Managed large volumes of data using Hive and HDFS in a Hadoop ecosystem
• 🔹 Implemented data quality checks and data validation frameworks
• 🔹 Collaborated with cross-functional teams (Data Scientists, Analysts, Product) for scalable data delivery
• 🔹 Contributed to end-to-end data warehousing projects

Mar 2022 – Apr 2024 · 2 yrs 1 mo · Bengaluru, Karnataka, India

• 🔹 Migrated legacy data pipelines from SAS to Google Cloud Platform (GCP) using BigQuery, Cloud Composer (Airflow), and Dataflow
• 🔹 Converted SAS ETL logic to Python & SQL scripts for compatibility with GCP-native tools
• 🔹 Rewrote complex PROC SQL and SAS macros into optimized BigQuery SQL and Dataform
• 🔹 Implemented GCS-based ingestion pipelines replacing FTP-based SAS loads
• 🔹 Ensured data quality validation using Great Expectations and custom GCP validation scripts
• 🔹 Reduced pipeline cost & runtime by leveraging serverless architectures in GCP
• 🔹 Built and optimized distributed data processing jobs using PySpark for millions of records daily
• 🔹 Created reusable modular PySpark functions for data cleansing, transformation, and joins

Apr 2021 – Mar 2022 · 11 mos · Bengaluru, Karnataka, India

• 🔹 Developed and maintained end-to-end ETL pipelines to ingest, transform, and load banking data from multiple legacy systems into a centralized data lake
• 🔹 Migrated workflows from on-prem SAS systems to cloud-based architecture (GCP/BigQuery)
• 🔹 Translated business requirements into data transformation logic using PySpark, SQL, and Python
• 🔹 Worked with sensitive financial data including transaction records, credit scoring, and fraud indicators – ensured data masking & encryption standards were met
• 🔹 Designed and implemented data validation frameworks to ensure regulatory compliance (e.g., SOX, Basel)
• 🔹 Collaborated with cross-functional teams including Capgemini offshore team, client SMEs, and business analysts across time zones
• 🔹 Automated SAS reports into dashboard visualizations using Looker/Tableau integrated with BigQuery
• 🔹 Optimized ETL jobs to reduce SLA breach risk by 30% and improved job monitoring with Airflow and Stackdriver
• 🔹 Documented technical workflows and created data dictionaries for reusable assets

Sep 2019 – Feb 2020 · 5 mos · Maharashtra, India

• 🔹 Built and trained ML models for classification, regression, and clustering tasks using Scikit-learn, XGBoost, and TensorFlow
• 🔹 Developed end-to-end data pipelines for model training and evaluation — from data ingestion to deployment
• 🔹 Cleaned and preprocessed large datasets using Pandas, NumPy, and feature engineering techniques
• 🔹 Conducted exploratory data analysis (EDA) and generated actionable insights using Seaborn, Matplotlib, and Plotly
• 🔹 Applied NLP techniques like sentiment analysis, TF-IDF vectorization, and topic modeling for text datasets
• 🔹 Deployed machine learning models via Flask, Streamlit, or FastAPI for interactive web-based demos
• 🔹 Tuned hyperparameters using GridSearchCV and evaluated model performance using metrics like F1-score, AUC, and RMSE
• 🔹 Collaborated with cross-functional teams to translate business problems into data-driven solutions
• 🔹 Documented model decisions, assumptions, and created visual reports for stakeholders

Jun 2019 – Nov 2019 · 5 mos · Greater Bengaluru Area

• 🔹 Built multiple ML models – classification, regression, clustering using Scikit-learn, TensorFlow, and XGBoost
• 🔹 Completed hands-on projects like:
• Spam Email Detection
• House Price Prediction
• Customer Churn Prediction
• 🔹 Worked with real datasets from Kaggle, UCI, and open-source APIs
• 🔹 Deployed models via Flask & Streamlit on Heroku
• 🔹 Applied NLP techniques for sentiment analysis and topic modeling
• 🔹 Performed EDA & feature engineering using Pandas, NumPy, and Seaborn
• 🔹 Version controlled projects using Git & GitHub
• 🔹 Completed certifications: e.g., Google, Microsoft

Jan 2018 – Jun 2018 · 5 mos · Greater Hyderabad Area

• 🔹 Assisted in performing vulnerability assessments and penetration testing on web applications and internal systems
• 🔹 Conducted network traffic analysis using Wireshark to identify potential threats and anomalies
• 🔹 Supported implementation of firewall rules, intrusion detection systems (IDS), and SIEM dashboards
• 🔹 Participated in incident response simulations, documented root causes, and proposed mitigation plans
• 🔹 Automated security checks using Python scripts for log analysis and malware signature detection
• 🔹 Helped design and run phishing awareness campaigns and internal cybersecurity training for employees
• 🔹 Reviewed and updated security policies and compliance checklists based on ISO 27001 best practices
• 🔹 Collaborated with senior security analysts on real-time threat monitoring and log triage
• 🔹 Documented security incidents and maintained weekly audit logs and risk assessment reports