👋 Jarosław Błaziński – Analytics Engineer / Data Engineering Portfolio

Analytics Engineer or Junior / Mid Data Engineer with hands-on experience in PySpark, SQL Server, Apache Airflow, and Databricks.
Projects focus on practical data challenges: pipeline automation, ETL/ELT processing, Medallion Architecture, data quality, and governance.

📧 j.blazinski@gmail.com · 💼 LinkedIn · 🐙 GitHub

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🗂️ Projects

1. 💱 Frankfurter API – Airflow DAG for Currency Rates

File: 1_Frankfurter_api_currencies.py
Tech: Python · Apache Airflow · REST API

A scheduled Airflow DAG (runs every Monday) that fetches exchange rates for 6 currency pairs from the European Central Bank's public API and saves them to a CSV file ready for analysis.

Key concepts: REST API integration · DAG scheduling · error handling & logging

📄 Details: 1_Frankfurter_api_currencies_README.md

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2. ⚡ PySpark ETL Pipeline – E-commerce Transaction Analytics

Files: 2_PySpark_ETL.ipynb · 2_input_data.csv
Tech: PySpark 3.x · Python 3.8+ · Jupyter / Databricks

An end-to-end ETL pipeline processing ~500,000 transaction records from European markets (PL, DE, DK, SE). Covers data quality validation, cleansing, dimensional enrichment, and advanced analytics using window functions.

Key concepts: DataFrame API · window functions (dense_rank) · joins & aggregations · null/negative value validation · Databricks / Delta Lake readiness

📄 Details: 2_PySpark_ETL_README.md

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3. 🔄 CDC Demo – Change Data Capture in SQL Server

File: 3_CDC_Demo_SQLServer.sql
Tech: SQL Server 2016+ · T-SQL

A complete demo of enabling and querying CDC on SQL Server. Captures INSERT / UPDATE / DELETE changes, builds an audit trail, and queries net changes per record.

Key concepts: CDC configuration (sp_cdc_enable_db, sp_cdc_enable_table) · __$operation interpretation · before/after audit trail · net changes queries

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4. 🥉 ELT Bronze Layer – Raw Data Ingestion

File: 4_ELT_Bronze.ipynb
Tech: PySpark · Databricks · Delta Lake

First layer of the Medallion Architecture. Ingests raw data from source systems into the Bronze zone with minimal transformation — preserving data as-is with added metadata (ingestion timestamp, source).

Key concepts: Medallion Architecture · raw zone design · Delta Lake write · schema-on-read

📄 Details: 4_ELT_Bronze_README.md

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5. 🥈 ELT Silver Layer – Cleansed & Conformed Data

File: 5_ELT_Silver.ipynb
Tech: PySpark · Databricks · Delta Lake

Second layer of the Medallion Architecture. Applies data cleansing, deduplication, type casting, and business rules to produce a reliable, conformed dataset.

Key concepts: data cleansing · deduplication · SCD handling · Delta Lake merge (UPSERT)

📄 Details: 5_ELT_Silver_README.md

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6. 🥇 ELT Gold Layer – Business-Ready Aggregations

File: 6_ELT_Gold.ipynb
Tech: PySpark · Databricks · Delta Lake

Third and final layer of the Medallion Architecture. Produces aggregated, denormalized tables optimized for reporting and analytics consumption.

Key concepts: aggregation logic · star schema design · Delta Lake optimization · BI-ready output

📄 Details: 6_ELT_Gold_README.md

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7. 🏛️ Data Governance

Files: 7_Data_Governance.ipynb · data_catalog.md
Tech: PySpark · Databricks · Delta Lake

Demonstrates core data governance practices applied across the Medallion pipeline: data cataloging, lineage tracking, quality rule enforcement, and access control patterns.

Key concepts: data catalog · column-level lineage · data quality checks · PII handling · Unity Catalog concepts

📄 Details: 7_Data_Governance_README.md

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🛠️ Tech Stack

Area	Technologies
Big Data	PySpark 3.x, Databricks, Delta Lake
Architecture	Medallion (Bronze / Silver / Gold)
Orchestration	Apache Airflow
Databases	SQL Server, T-SQL
Data Governance	Data Catalog, Lineage, Quality Rules
Languages	Python 3.8+, SQL
Tools	Jupyter Notebook, Git

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📝 Note

All projects are portfolio demonstrations — not production code from previous employers.
The goal is to showcase technical skills, business thinking, and knowledge of data engineering best practices.