10+ yrs in data engineering • AWS • Databricks & Spark • PySpark • Trino • Apache Ranger • Terraform • Kubernetes
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- Building reliable platform data engineering foundations (governance, scalability, cost).
- Modern DE stack: Spark, Delta/Parquet, Trino, dbt, Airflow, Kafka, Terraform, AWS.
- Secure data access with Apache Ranger and policy-driven controls.
Python · PySpark · Spark SQL · Databricks · Delta · Kafka · Airflow · Trino · Apache Ranger · dbt · Postgres · S3 · Glue · Terraform · Docker · Kubernetes · Great Expectations
Trino + Apache Ranger on Kubernetes (Helm)
- Custom images, init-containers, and StatefulSet deployment for Trino with Ranger plugin.
- Solves real issues: missing
cred.jceks, JVM opts updates,ranger.service.namevalidation, and config mounts. - Built for repeatable, secure platform setups.
Spark Learning Lab
- Practical notebooks on DataFrame APIs, Structured Streaming, optimization.
- Companion to my “Spark Deep Dive” study plan.
PySpark Samples
- Hands-on patterns for joins, windowing, UDFs/UDAs, and incremental data processing.
- Apache Ranger × Trino deep-dive (design, gotchas, Helm) — coming soon on k2ddna.com.
- Data platform patterns for secure multi-tenant analytics.
- Databricks Certified Data Engineer Associate (Dec 2024)
- Built OCSF-aligned feed mappings and pipelines; working on Benthos ingestion and policy-guarded changes.
If you’re building secure, scalable data platforms (Ranger, Trino, Spark, Terraform on AWS/K8s), I’m always up for pairing on design reviews, POCs, and OSS improvements.


