Target: Databricks — Data Engineer · Resident Solutions Architect

Lakehouse builder.
Customer-facing architect.
Ready for Databricks.

I'm Saikrishna Poluri — Lead Data Engineer with 9+ years architecting cloud data platforms, and the last several of them building production lakehouses and GenAI agents on Databricks for enterprise finance and HR. This site is both my portfolio and the working knowledge base I use to prepare for Databricks Data Engineer and Resident Solutions Architect roles.

9+ yrs data engineering 3 production GenAI agents on Databricks 7 certifications (Microsoft + Databricks + Dremio) 5 enterprise lakehouse programs delivered CFO-level stakeholders served
View portfolio Start the DE track Start the RSA track

Two target roles, one body of evidence

TRACK 01 · 10 MODULES

Data Engineer Track

Spark internals, Delta Lake, streaming & Auto Loader, Lakeflow, Unity Catalog, modeling, performance & FinOps, Databricks SQL, DevOps, and the certification roadmap — every module tied back to production work I can speak to in an interview.

 TRACK 02 · 6 MODULES

Resident Solutions Architect Track

The RSA role decoded, migration playbooks, reference architectures, GenAI on Databricks, consulting craft, and fully worked customer case studies — the customer-facing half of the Databricks field-engineering interview.

 TRACK 03 · 4 MODULES

Interview Prep Track

PySpark & SQL drills, data-platform system design, a STAR story bank built from my real projects, and an interactive 8-week study plan with progress tracking.

Why Databricks, and why me

I already ship on the platform

Medallion pipelines on Azure Databricks integrating SAP, JDE, HFM, DB2, and PeopleSoft into Delta Lake under Unity Catalog governance — plus three production GenAI agents (Genie Space, Vector Search RAG, multi-agent orchestrator) hosted as a Databricks App.

I work the way RSAs work

Discovery with Finance, Accounting, Tax, Audit, HR, and IT stakeholders; fit-gap and architecture recommendations; runbooks, hypercare, and enablement; mentoring engineers through design and code reviews.

I treat cost as a feature

Databricks FinOps in production: cluster right-sizing, autoscaling policies, Delta tuning (partitioning, compaction, Z-ORDER), and semantic-layer architecture decisions with multi-million-dollar infrastructure impact.

How to use this site

Recruiters and hiring managers: start with the portfolio. Future me (and fellow candidates): the tracks are ordered — work the DE track for platform depth, the RSA track for the customer-facing skillset, then drill with the prep track and check off the 8-week study plan as you go. Knowledge pages flag story hooks (answer from experience), likely interview questions, and pitfalls throughout.