Could you provide a brief introduction about yourself, describe your project and skills? Explain your project: what was the ingestion process, how did you ingest the data, how did you process it, what downstream steps did you take, and what orchestration tools did you use with S3?

Sure. I’m a data engineer with 4 years of experience building end‑to‑end pipelines on AWS. In my last project, I built a real‑time customer‑behavior pipeline for an e‑commerce platform. Situation: The company had fragmented logs in S3 and needed a unified view for analytics. Task: Design a scalable ingestion and processing workflow that could handle 10 GB/day. Action: I used AWS Glue Crawlers to discover schema, then scheduled Glue ETL jobs to transform raw JSON into Parquet, applying deduplication and enrichment. For orchestration, I leveraged AWS Step Functions to coordinate Glue jobs, Lambda for error handling, and CloudWatch for monitoring. Downstream, the curated Parquet files were loaded into Snowflake via Snowpipe for BI reporting, and a SageMaker endpoint consumed the data for churn prediction. Result: The pipeline reduced data latency from 24 h to 5 min, increased data quality, and cut storage costs by 30 %. My key skills include AWS Glue, Step Functions, Snowflake, Spark, and Python.