Marko Madic

Designed a BigQuery-based cloud data warehouse, leveraging dbt for scalable transformations that improved data lineage and availability while supporting reporting and operational workflows.  Led a team of 4 to automate partner financial reporting and invoicing workflows, eliminating manual data entry for executive and business stakeholders.  Built dbt models and Airflow ELT workflows for ML training-data preparation, reducing manual data cleaning effort by 20+ hours per week for data science teams.

Redesigned a Snowflake data mart using dimensional modeling best practices, migrating to a star schema and reducing Tableau load times from 2 minutes to 15 seconds.  Built 10+ Tableau dashboards used by 100+ stakeholders, enabling faster self-service reporting and decision-making.  Built an AWS pipeline (SES, Lambda, S3) to ingest partner reports delivered via email, automatically parsing, attributing, and transforming them into upstream-ready format, eliminating manual report collection entirely

 Built Python-based connectors and ingestion pipelines, expanding reporting coverage and processing 5GB+ daily.  Developed dbt models to create production-ready presentation-layer tables for analysts and business reporting.  Optimized Airflow jobs and SQL transformations, reducing pipeline runtime from 4 hours to 2 hours and lowering compute costs.  Automated partner data ingestion, reducing a manual process from 7 days to 30 minutes.