BI Developer | Senior Data Analyst

9/2023 – 6/2024

• Migrated a vast SAP BusinessObjects environment with thousands of reports to Google Looker, enhancing interactivity and analytical capabilities for fund managers.
• Developed the Looker analytics architecture and visualizations, including LookML models, explores, views and dashboards for financial investment reporting (e.g. fund assets, private equity, ESG).
• Implemented ETL processes between Looker and the data warehouse, transforming DWH data into aggregated tables and data models tailored to meet business needs.
• Built Python scripts to harness Looker’s API and integrated Looker with GitHub for version control.
• Leveraged HTML and Liquid to design custom navigation, filters, and metrics, improving the user experience of interacting with the data and dashboards.
• Trained an in-house team of analysts and project managers on Google Looker and authored documentation to support independent usage.

Business Intelligence (BI), Data Warehousing, Datenanalyse, Google Cloud, Python, SAP BusinessObjects (BO), SQL

12/2022 – 6/2023

• Designed, trained and deployed an XGBoost machine learning model in Python to predict market indicators, driving automated trading strategies.
• Implemented a real-time end-to-end pipeline to fetch live API data, transform it into a feature data frame, and deliver predictions to the algorithm engine for instant trading decisions.
• Created interactive Tableau dashboards and Jupyter notebooks to visualize and analyse algorithm performance, enhancing transparency for business owners.

Data Science, Datenanalyse, Maschinelles Lernen, Python, Scikit-learn, Tableau

1/2022 – 7/2022

• Established the analytics environment for a new product line, developing Looker and Metabase dashboards, data models, and event trackers to support informed decision-making by stakeholders.
• Forecasted operational needs and identified bottlenecks affecting customer wait times, leading to an operational overhaul and a 50% reduction in wait times.
• Achieved a 5% increase in conversion rates through A/B testing, enhancing product revenue.
• Laid the groundwork for LTV analysis by clustering the user base into customer value segments and ranking features for value generation using XGBoost and SHAP.
• Led a weekly data deep-dive meeting with the project team to share insights and align findings with product and business strategies.

Big Data, Business Intelligence (BI), Data Science, Data Warehousing, Datenanalyse, Jira, Lean Startup, Maschinelles Lernen, Predictive Analytics, Python, Snowflake, SQL