Stanislav Kraun

I’m a Senior Data Engineer delivering production performance and reliability improvements across Databricks and Snowflake workloads, with a track record of keeping business logic and reporting outputs stable while cutting costs. I’ve owned execution changes that produced over $300,000 annualized compute savings, without breaking the downstream interpretation of data.

In recent fintech work, I eliminated 82B-row Snowflake ledger query latency—reducing analyst wait time from ~10 minutes per query to 15–30 seconds—while also cutting scan volume by 99%. I rebuilt failing Databricks AML screening under production load (OOM errors) into a stable zero-spill execution model, and I improved SLA by 6x by moving a Snowflake dashboard refresh from 30-minute latency back to 5 minutes using incremental processing.

Before that, I built end-to-end data pipelines at scale, including ingestion from AWS S3, Snowflake transformations, Apache Spark processing, and orchestration with Apache Airflow. I design for incremental loads, CDC, backfills for late-arriving events, data validation/quality, and schema evolution using modern lakehouse patterns and infrastructure automation (Terraform, CI/CD) to ensure repeatable delivery.