Google BigQuery Expert in Germany

• Product Analytics, Mobile App Tracking & Tracking Governance (B2C Mobility App) – agile project management (Scrum/Kanban)
• Product Ownership for Product Analytics and Mobile App Tracking of the EnBW mobility+ app; gathering, prioritizing, and translating business requirements into actionable concepts and Azure DevOps user stories with acceptance criteria.
• Derivation of tracking requirements when introducing new app features (including Resilient Map), definition of tracking parameters (screens, events, custom definitions), and ensuring privacy-compliant tracking (Firebase, GA4, Adjust) based on the tracking concept.
• Design and adaptation of dashboards and funnel reporting for campaigns (GA4 validation, onboarding and order flow analyses, conversion funnels, charging start flow) to identify drop-off points and optimization potential.
• Management of the technical raw data export (Adjust to BigQuery) and connection to the data warehouse/data lakehouse, including data mapping; collaboration with international development teams, Data Engineering, Marketing/Sales, and Product Management.
• Establishment of standardized tracking architecture, naming conventions, and governance; analysis and expansion of tracking (new features and “blind spots”), test design, handover to testers, and quality assurance and approval before releases; documentation in Conceptboard.

• Marketing Strategy: Lead the end-to-end analysis and evaluation of cross-channel marketing campaigns across the entire Customer Journey. My focus is identifying optimization potential and deriving clear, actionable recommendations that drive measurable business impact.
• Data Science & AI: Advanced predictive modeling (Churn, LTV), market basket analysis, clustering, and real-time AI-powered audience discovery utilizing RAG/LLMs.
• Marketing Analytics & Measurement: End-to-end attribution analysis, Marketing Mix Modeling (MMM), audience segmentation, conversion path analysis, and A/B testing across all major platforms.
• Data Engineering & Reporting: Designing and managing robust, multi-platform data pipelines (BigQuery, GCP) for data consolidation, automated dashboard generation, and critical API integrations.

• This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.

Used: Python, RAG, LangGraph, LangChain, deepeval, MCP

• Client — Podimo ApS (podcast & audiobook streaming): build the finance reporting layer on BigQuery + dbt + Airflow, including the core revenue-transaction fact tables used across finance reporting.
• API automation: design and build a BigQuery → Airflow → Microsoft Dynamics 365 Business Central REST-API pipeline to automate sales-invoice posting, with idempotency and master-data sync between systems.
• Delivery: sole engineer on the engagement — requirements, modelling, orchestration and stakeholder communication with the client finance team, end to end.

• Analysis of the existing reporting platform, including processes and governance topics, with stakeholders from Sales and Marketing.

• Detailed analysis and evaluation of customer-defined requirements for existing reporting and new dashboards to be established.

• Support for stakeholders in managing sales processes and identifying KPI trends at an early stage.

• Use of Microsoft Power BI as the central analysis and reporting platform.

• Development of a proposal for the required further development of processes and the Power BI platform.

• Documentation of AS-IS business processes and derivation of company-wide KPIs, including coordination with stakeholders.

• Analysis and assessment of the customer's Power BI platform.

• Analysis of processes and data governance.

• Documentation of AS-IS business processes.

• Development of recommendations for further developing processes and the reporting platform.

• Design and implementation of dashboards in Power BI.

• Development of company-wide KPIs and coordination with stakeholders.

• Microsoft Power BI.

• Data analytics.

• KPI definition and reporting.

• Dashboard design and data visualization.

• Stakeholder management and requirements management.

Specialize in turning complex data from diverse environments into actionable business value through compelling visual storytelling. I am an expert in generating actionable insights and presenting recommendations to business stakeholders. My technical proficiency in SQL, Python, and leading data visualization tools like Tableau and Power BI allows me to deliver a new generation of self-service tools and analytics services.

• Data Visualization & Storytelling: Created impactful data visualizations and dashboards in Tableau and Power BI, effectively communicating findings and presenting actionable recommendations to C-suite stakeholders and business leaders.
• Stakeholder Management: Built effective working relationships with key business stakeholders, data engineers, and other partners to achieve common data-driven goals and targets.
• Insights & Recommendations: Generated actionable insights from complex data analysis for funnel conversion, marketing performance, and ROI, directly influencing business performance and strategy.
• Data Collaboration & Empowerment: Worked closely with cross-functional teams to support the ongoing data needs of internal partners, helping to optimize internal data processes and workflows.
• BI & Data Expertise: Applied extensive experience in data modeling, data collection, data mining, and analysis to deliver end-to-end analytical solutions from stakeholder discovery to production.

• Led end-to-end delivery of ML/AI applications for the QuantistryLab simulation platform, from requirements definition and solution design to development, testing, deployment, and production support, coordinating efforts across back-end, DevOps, and ML teams
• Designed and implemented a compound search system with an open-weights LLM, the Pydantic AI agent framework, and the Model Context Protocol (MCP), exposing it through FastAPI endpoints
• Deployed and configured vLLM-based LLM serving on Kubernetes
• Built CI/CD pipelines and Helm charts to automate deployments across staging and production environments
• Instrumented the search system with OpenTelemetry spans and traces, integrating with the Honeycomb observability platform to monitor component-level latency and functional errors
• Developed a custom evaluation dataset and implemented evaluations for retrieval relevance, answer correctness, tool-call accuracy, and end-to-end task success
• Reduced infrared spectrum prediction time by 83% (from 60 to 10 minutes) by replacing a quantum-mechanical model with a pretrained graph neural network; fine-tuned the model using PyTorch, built and containerized the inference pipeline with Docker, and integrated it into the platform
• Implemented an MLflow model registry with MinIO-backed artifact storage
• Advised sales and science teams on ML/AI applications for prospective client projects, providing technical expertise to shape proposed solutions and prototypes

Technologies: AWS, Hetzner, Kubernetes, Helm, Docker, vLLM, Pydantic AI, MCP, Qwen, Llama 3, MLflow, MinIO, GitHub Actions, Python, FastAPI, OpenTelemetry, Honeycomb, PyTorch, Polars, pytest

Results:

• Identified weaknesses in the MMM setup and attribution as a whole

Responsibilities:

• Audits of Marketing Mix Modelling, Multi-Touch-Attribution, Incrementality Testing

Channels & Tools: SEA, Affiliate, TV, Adtriba MTA & MMM, Google Meridian MMM

Statistical Forecasting Algorithm

• Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
• Migration from R/On-premise to Python/Snowflake
• Productionalization on Snowflake in cooperation with data engineers & DevOps

Monitoring Dashboard

• Data engineering for preparation & provisioning of necessary data/resources on Snowflake
• Development & deployment of a Streamlit dashboard in Snowflake

ML-based Probabilistic Forecasting on Vertex AI

• Development of a ML-based probabilistic forecasting algorithm from scratch
• Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI

Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow

• Cut first-30-day post-subscription churn 45% to 20% by revamping onboarding and optimizing time-to-value.
• Drove 3x LTV in 6 months through retention and monetization experiments across the customer lifecycle.
• Owned app redesign and feature delivery leading to lifting active-user NPS from 6.3 to 8.5.

• Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
• Implementation of automated end-to-end cloud processes
• Development of LLM and NLP models
• Creation of interactive reports
• Support for national and international large corporations as well as medium-sized companies in implementing ML projects

• Defined the vision, strategy, and roadmap of AI-powered analytics products, ensuring they met the business needs of Sales, Marketing, Finance, and executive teams across Europe.
• Collected business requirements, prioritized AI product features, and led Agile development of forecasting and analytics solutions. Defined product specifications, user stories, and acceptance criteria to ensure successful delivery.
• Collaborated with business stakeholders, Product Owners, data scientists, ML engineers and software engineers to transform AI models into scalable business products and integrate AI insights into operational workflows.
• Designed and implemented Generative AI solutions leveraging Large Language Models (LLMs) to automate reporting and enable natural-language querying of enterprise data, reducing manual effort by approximately 30%.
• Defined product goals and success metrics, tracked product performance and user adoption, and continuously improved the product based on user feedback and business results.
• Established data governance, master data quality and reporting standards across SQL, BigQuery and Power BI environments to ensure reliable, secure and scalable analytics.

• Designed and implemented scalable ETL/ELT pipelines using Python, SQL, dbt, AWS and Informatica to ingest data from sources such as APIs, relational databases, and flat files into Snowflake, reducing pipeline runtime by ~30%.
• Migrated high-volume datasets from on-premises Teradata to Snowflake using AWS services (S3, Glue, Step Functions, IAM), ensuring data consistency and integrity.
• Applied Kimball methodology to design star and snowflake schemas, improving query performance and reducing Snowflake compute costs.
• Implemented automated data quality checks using SQL-based dbt tests and the Great Expectations framework to detect anomalies and enforce data correctness before production loads.
• Orchestrated ETL workflows in Airflow using Python and managed code deployments via Git with CI/CD best practices to increase deployment reliability and maintain pipeline uptime.
• Built interactive Power BI dashboards and curated datasets to enable data-driven decision-making for stakeholders.
• Maintained technical documentation in Confluence for ETL workflows, and led knowledge-sharing sessions for new joiners.

• Designed blockchain-based learning platform with event-driven architecture, integrating RESTful APIs for personalized learning paths
• Developed scalable microservices using Java, Python, and React in Kubernetes, enabling real-time data processing
• Implemented smart contracts (Solidity) for secure course purchases and mentor engagement
• Led AI-supported matching system (DecisionTreeRegressor) to optimize learning recommendations