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Data Engineer with less than a year in Data Analytics & Visualization
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BSc. (Hons) Computing graduate with First Class Honours and hands-on experience building production-style ETL/ELT pipelines, dimensional data warehouses, and cloud-based analytics solutions. Proficient in Python, SQL, Apache Airflow, PySpark, Snowflake, dbt, Docker, and REST API integration. Skilled in data modeling, Medallion Architecture, workflow orchestration, data quality validation, and developing scalable data platforms across AWS and Azure. Passionate about data engineering and leveraging modern data technologies to deliver reliable, analytics-ready solutions.
The British College
BSc. (Hons) Computing · Computing
September 1, 2021 – November 1, 2025
Himalayan WhiteHouse International College
+2 Science (Physics) · Science (Physics)
January 1, 2019 – December 31, 2021
Accenture North America
Data Analytics & Visualization Job Simulation (Forage)
June 1, 2025 – June 30, 2025
India
Flight Data Pipeline
June 21, 2026 – Present
Built a near real-time micro-batch ELT pipeline (30-min cadence) ingesting live global flight data from the OpenSky REST API, handling authentication, pagination, and incremental loads. Orchestrated end-to-end workflows using Apache Airflow DAGs with idempotent task design, scheduling logic, and failure-recovery patterns for reliable, uninterrupted data flow. Implemented Medallion Architecture (Bronze → Silver → Gold) for layered data transformation and quality enforcement across thousands of aircraft records per run. Loaded analytics-ready KPIs (flights per country, average velocity, on-ground %) into Snowflake for downstream BI consumption. Containerized the full pipeline using Docker for portable, reproducible deployment. Stack: Airflow, Python, Snowflake, Docker, REST API, SQL, Git
View ProjectMovieLens ELT Pipeline
June 21, 2026 – Present
Built an end-to-end cloud ELT pipeline processing the MovieLens 20M dataset (20M+ ratings, 138K+ users) using AWS S3, Snowflake, and dbt. Designed a multi-layer dbt architecture (staging, dimension, fact, mart) with Kimball dimensional modeling, incremental models, and SCD Type 2 snapshots. Integrated Snowflake with AWS S3 via IAM-based external stages; applied automated dbt tests and Jinja macros for data quality and maintainability. Stack: Snowflake, dbt, AWS S3, SQL, Jinja, IAM, Git
View ProjectPredictive Analytics Platform
June 21, 2026 – Present
Designed an end-to-end ETL pipeline using Databricks (PySpark) integrating heterogeneous sources into a Cloud Data Mart on Azure Synapse with strong data quality validation and error handling. Built time-series forecasting models for sales prediction; developed Tableau dashboards for product performance and regional analytics. Optimized processing using PySpark transformations and data partitioning; integrated a Flask web interface for accessible business user analytics. Stack: Python, PySpark, Databricks, Azure Synapse, SQL, Tableau, Flask, MongoDB, Git
View ProjectAccenture North America Data Analytics & Visualization Job Simulation
Forage
June 1, 2025 – Present
Cultural Fit Analysis
The candidate's personal projects demonstrate a proactive and self-driven approach to learning and applying data engineering concepts. The diversity of tools and platforms used (Airflow, Snowflake, dbt, PySpark, AWS, Azure) indicates adaptability and a willingness to explore different technologies, which aligns well with dynamic team environments. The focus on building complete, production-style pipelines suggests a results-oriented mindset.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude and an ability to design robust, scalable data solutions. The use of idempotent tasks, failure-recovery patterns, and automated testing suggests an operational mindset focused on reliability and maintainability. The detailed project descriptions also reflect good communication skills in articulating technical solutions.