Data Engineer — bridging classic data warehousing with the modern lakehouse.

I spent ~5 years deep in the Oracle DWH ecosystem (PL/SQL, ODI, dimensional modeling, SCD) building and optimizing enterprise data pipelines, primarily in the insurance domain. Now I'm carrying that foundation into the modern data engineering stack — CDC, streaming, and lakehouse architecture.

• Data Warehousing · Oracle, PL/SQL, ODI, star schema, SCD Type 2, MLOG-based CDC — production ETL/ELT at enterprise scale
• Modern Data Engineering · Kafka, Debezium (CDC), Spark Structured Streaming, dbt, Apache Iceberg, Airflow
• Lakehouse Architecture · End-to-end pipelines from source CDC to a queryable, ACID lakehouse with BI + ML on top
• Domain Expertise · Deep experience in insurance (claims, provisions, benefits, reconciliation)

A self-hosted, real-time e-commerce lakehouse, built end to end:

Postgres → Debezium (CDC) → Kafka → Spark Structured Streaming → MinIO/Iceberg → dbt → Airflow → Superset

• Real-time CDC with soft-delete handling and LSN-based deduplication
• Medallion architecture (bronze/silver/gold) on Apache Iceberg — ACID, schema evolution, time travel
• ML layer — fraud detection, demand forecasting, customer segmentation, churn prediction (feature store in dbt, orchestrated by Airflow)
• AI access layer — an MCP server enabling natural-language analytics over the lakehouse

Oracle PL/SQL ODI Python SQL dbt Apache Spark Apache Kafka Debezium Apache Iceberg Apache Airflow Superset Qlik Sense PostgreSQL Docker MinIO

Most of my earlier work lived in enterprise Oracle/ODI repositories — my recent GitHub activity reflects my move into the open, modern data stack.

LinkedIn: https://linkedin.com/in/deniz-isik-ofc/
E-mail: denizsk977@gmail.com