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katakuro83/README.md

Hi, I'm Katarzyna Kurowska

Data Product Manager acting as Data Architect who transforming raw data into scalable solutions

designing robust data architectures with optimized data pipelines on cloud data platforms.

About Me

I design data platforms that scale from raw ingestion to governed, analytics ready warehouses. My focus is architecture that's not just performant on day one, but maintainable, cost aware, and adaptable as data volume and business needs grow.

  • Architecture: dimensional modeling, medallion (bronze/silver/gold) lakehouses, data mesh principles
  • Pipelines: batch & streaming ELT/ETL, orchestration, CDC
  • Cloud Platforms: Snowflake, Databricks
  • Governance: data quality, lineage, cataloging, access control
  • Engineering: IaC, CI/CD for data, testing pipelines like software

Tech Stack

  • Languages & Query Python SQL

  • Orchestration Airflow dbt

  • Cloud & Warehousing AWS Azure GCP Snowflake Databricks

  • Streaming & Storage Kafka Delta Lake

  • Infrastructure Terraform Docker

Data Projects

Project Description Stack
Modern Data Warehouse on Snowflake End to end ELT pipeline: raw ingestion, staging, dimensional star schema, with automated testing and documentation. Snowflake, dbt, Airflow
Medallion Lakehouse Architecture Bronze/Silver/Gold lakehouse on Databricks with schema enforcement, data quality checks, and incremental (CDC) processing. Databricks, Delta Lake, PySpark
Real Time Streaming Pipeline Event driven pipeline ingesting streaming data, processing with Spark Structured Streaming, and landing into a queryable sink. Kafka, Spark Streaming, Docker
Cloud Data Platform as Code Fully reproducible cloud data platform (warehouse, storage, IAM, networking) defined and deployed via IaC with CI/CD. Terraform, AWS/Azure, GitHub Actions
Data Modeling Playbook Reference repo comparing modeling approaches: 3NF, Kimball star schema, Data Vault 2.0 with the same dataset, plus decision guidance on when to use each. SQL, dbt, ERD diagrams
Data Quality & Observability Framework Framework for validating pipeline outputs, tracking data lineage, and alerting on anomalies. Great Expectations, dbt tests, Airflow

Let's Connect

Feel free to reach out if you'd like to connect, share ideas, or discuss potential future collaborations. I'm always open to discussing data architecture, pipeline design or cloud data platform strategy, as well as exploring potential career opportunities as a Data Product Manager or Data Architect.

LinkedIn: kurowskakatya

Pinned Loading

  1. modern-data-warehouse-snowflake modern-data-warehouse-snowflake Public

    End to end Data Warehouse project using Snowflake, dbt and Apache Airflow.

  2. medallion-lakehouse-databricks medallion-lakehouse-databricks Public

    Bronze/Silver/Gold lakehouse on Databricks with schema enforcement, data quality checks, and incremental (CDC) processing.

  3. real-time-streaming-pipeline-kafka real-time-streaming-pipeline-kafka Public

    Event driven pipeline ingesting streaming data, processing with Spark Structured Streaming, and landing into a queryable sink.

  4. cloud-data-platform-terraform cloud-data-platform-terraform Public

    Fully reproducible cloud data platform (warehouse, storage, IAM, networking) defined and deployed via IaC with CI/CD.

  5. data-modeling-playbook-dbt data-modeling-playbook-dbt Public

    Reference repo comparing modeling approaches: 3NF, Kimball star schema, Data Vault 2.0 with the same dataset, plus decision guidance on when to use each.

  6. data-quality-observability-framework data-quality-observability-framework Public

    Framework for validating pipeline outputs, tracking data lineage, and alerting on anomalies.