Help & Supportcareers@gravityer.com
Data Engineer with 5+ years in cloud data warehousing and ETL pipelines.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
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.