Data Engineer with 5+ years in cloud data warehousing and ETL pipelines.
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Data Engineer with 5 years designing robust ETL pipelines and cloud data warehouses supporting advanced analytics and machine learning workflows across high-scale financial environments. Proficient in Python, SQL (Structured Query Language), AWS (Amazon Web Services) including S3, Redshift, Glue, and Lambda, with Spark-based big data technologies processing 100M+ monthly transactions at 97% data precision. Brings proven capability to build scalable, cost-efficient data solutions enabling fraud, waste, and abuse detection in a fast-paced setting.
Sacred Heart University
Master of Science · Computer and Information Science
August 1, 2023 – June 30, 2024
Presidency University
Bachelor of Technology · Engineering (Petroleum Engineering)
August 1, 2018 – June 30, 2022
JP Morgan Chase
Data Engineer
August 1, 2024 – Present
Jersey City, New Jersey, United States
S&P Global
Data Engineer
February 1, 2022 – August 1, 2023
Bengaluru, Karnataka, India
Genpact
Data Analyst
November 1, 2020 – January 1, 2022
USA
Oil and Natural Gas Corporation
Data Analyst Intern
June 1, 2019 – May 1, 2020
Bengaluru, Karnataka, India
AWS Lakehouse Pipeline: dbt Core Medallion Architecture with Delta Lake
June 22, 2026 – Present
Built end-to-end AWS lakehouse using dbt Core medallion layers and Databricks Delta Lake with Z-Ordering, ingesting 10M+ accounts via AWS MWAA Airflow. Implemented CI/CD automation for dbt model deployment via Git with schema testing, Stonebranch workload scheduling, and full data lineage tracking, achieving 97% data precision with end-to-end governance enforced.
Streaming Ingestion Pipeline: Kafka and Kinesis-Pattern Real-Time ELT on Databricks
June 22, 2026 – Present
Designed Kafka and Kinesis-pattern streaming ETL pipeline on Databricks Delta Lake and Amazon Redshift. Automated deployment using CI/CD Git workflows and Python scripting with dbt data quality governance, data lineage tracking, and documentation standards enforced end-to-end across all production pipeline environments.
Cultural Fit Analysis
The candidate's experience across multiple companies (JP Morgan Chase, S&P Global, Genpact) and diverse projects, including personal lakehouse and streaming pipelines, indicates adaptability and a proactive learning attitude. The focus on data governance, quality, and automation aligns well with best practices in data-driven organizations. The breadth of technologies and problem-solving scenarios encountered suggests a candidate who can thrive in dynamic and challenging environments.
Soft Skills & Operational Fit
The candidate's resume highlights collaboration with data scientists and product teams, indicating good teamwork and cross-functional communication skills. Experience with Agile methodology, JIRA, and CI/CD workflows suggests a strong operational fit for modern data engineering practices. The ability to troubleshoot and tune production pipelines demonstrates problem-solving and reliability-focused operational skills.