Ex-Amazon Data Engineering Architect Mentor

This session is for Data Engineers preparing for Senior/Staff-level system design rounds (Amazon, Databricks, Atlassian, fintech, product companies).

I bring 15+ years of experience across Visa, Amazon, Honeywell and others, where I’ve:

• Led architecture and deployment of SPDTE, a unified ETL platform handling 350+ datasets at 600+ TPS with 30X scalability improvements.

• Designed and optimized 600+ TB analytical warehouses and real-time anomaly detection pipelines using AWS Glue, EMR, Redshift, SageMaker, and QuickSight.

• Built metadata-driven and agentic-AI–assisted frameworks for personalization, QC, and Airflow operations.

What we can cover in a 1:1:

• How to design end-to-end data systems in interviews (requirements → high-level architecture → deep dives).

• System design patterns for data engineering: batch vs streaming, CDC, lakehouse, dimensional models, serving layers.

• Mock system design interview on a scenario (e.g., clickstream analytics, payments data platform, feature store, real-time monitoring).

• Feedback on your approach: structure, trade-off discussions, tech choices (Spark, Kafka, Redshift/Snowflake/Lakehouse).

• How to turn your real projects into strong, Staff-level system design stories.

Ideal for you if:

• You’re targeting Senior / Staff / Principal Data Engineer roles and want your system design to feel “production-grade,” not theoretical.

• You come from strong data implementation background but struggle to structure or communicate designs clearly in interviews.

• You want practical, example-driven guidance from someone who has actually owned these systems end-to-end.

Outcome from a session:

• A clear, reusable framework for approaching any DE system design problem.

• Specific architecture diagrams and flows you can reuse and adapt.

• Concrete next steps and practice problems tailored to your experience and target companies.

You can also use subsequent sessions for follow-up mocks, design review of your take-home tasks, or deep dives into particular areas (streaming, warehousing, ML pipelines, or cost/performance tuning).