Data engineer in Bangalore. I build pipelines that land messy data, test it, and refuse to publish a number that fails a check — Python, SQL, PySpark, dbt, Kafka and AWS.
Portfolio: yashu24-coder.github.io
Right now I am working the stack end to end: batch on AWS, streaming with Kafka and Spark, and the dbt transformation layer on top. Four projects, each runnable from a clone, each with the tests and the numbers to back it up.
| Project | What it demonstrates |
|---|---|
| aws-retail-data-platform | Batch ELT on AWS — S3 medallion layers, Glue PySpark, Athena and Redshift Serverless, 12 data-quality gates, 23 tests, everything in Terraform |
| streaming-orders-pipeline | Kafka to Spark Structured Streaming — 120s watermark, 5-minute tumbling windows, exactly-once sink, dead-letter queue with reason codes |
| dbt-saas-analytics | dbt analytics engineering — 13 models, 27 data tests, incremental MRR fact table, SCD2 snapshot, cohort retention that reconciles with revenue |
| aws-nyc-taxi-data-engineering | NYC TLC lakehouse — S3 bronze/silver/gold, Glue, Athena and Terraform |
Every repository has a "What this project does not do" section. Start there. It says which parts run in CI and which need Docker or an AWS account, and it says plainly that the data is synthetic or public. I would rather you knew the edges than found them.
Python SQL PySpark dbt Kafka AWS (S3, Glue, Athena, Redshift)
Terraform Docker DuckDB pytest GitHub Actions
- Portfolio: yashu24-coder.github.io
- Email: yashwanthroyal9133@gmail.com
- LinkedIn: yashwanth-royal-kavali
- GitHub: @Yashu24-coder