Apache Spark Experts in Berlin

• Built a JTBD-based prioritization framework for 3,000+ accumulated feature requests, identified 27 broker jobs, validated 8 through 25 user interviews, and used the resulting job map as a live prioritization filter for all incoming channels (Upvoty, CSAT, consulting tickets).
• Responsible for the Scout24 Lighthouse initiative: Document Intelligence with full RAG architecture (semantic chunking, bge-m3 embeddings, pgvector, BM25+Dense hybrid retrieval).
• Reduced lead time of customer feature requests to 3.1 days through code analysis, ticket specification, and independent implementation using a coding agent (Codex).
• Developed an LLM-based support agent (GPT-4o mini, Codex-generated merge requests) that reduced 3rd-level escalations from 40% to 5% of all monthly tickets.
• Integrated six partners through technical coordination, specification, backlog and release management, and led seven full stack developers.
• Eliminated regulatory exposure for brokers in six weeks through risk analysis (BGH ruling on distance selling/GDPR), new audit features, and coordination with legal and data protection officers.

Tasks: Design and operation of end-to-end cloud data platforms for enterprise clients in publishing and finance, including infrastructure automation, pipeline development, monitoring, and data quality.

Activities:

• Built multi-layer data architectures on Databricks (Apache Spark, Delta Lake), BigQuery, and GCP
• Fully automated cloud infrastructure with Terraform across 3 environments (DEV/STG/PRD)
• Developed automated data pipelines with Python, dbt, and GCP services for different data sources
• Built monitoring and alerting systems for real-time platform monitoring
• Implemented data versioning and quality checks at every layer
• Designed automated test and deployment pipelines in GitLab and Bitbucket

Achievements:

• 2× production data processing capacity, reduced spike response time from minutes to ≤15 s, server errors ≈ 0
• Replaced 3,000 lines of manual configuration with a reusable automation module for 7 customer domains, configuration errors to 0
• Delivered a complete end-to-end data platform at ~€10/month infrastructure cost
• Migrated 7 database tables with 0 downstream issues
• Removed 100% exposed credentials, eliminated external vendor dependency
• Delivered integration of 3 teams in 1 sprint

• Leading coordination between business units and IT
• Coordinating development and testing
• Business analysis and structured requirements gathering
• Specifying functional and technical requirements
• Integrating interfaces to internal systems
• Developing SQL queries and reports
• Documentation in Confluence Result: On-time go-live, structured and agreed project basis, ensuring a coordinated project workflow.

• Providing freelance software engineering and AI orchestration services for an early-stage startup.
• Designing and coordinating autonomous AI systems capable of executing complex, multi- step workflows.
• Developing customer-facing pilots and proof-of-concept solutions.
• Participating in meetings with customers and investors to support product development and business discussions.

• Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
• Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.

• Crypto quant strategy development, automated trade execution, onchain data client (Ethereum / Solana)
• Data and trade architecture development for liquidity provision

• Context: Healthcare and laboratory teams required faster document analysis, treatment-planning support, and reliable AI workflows for MR/VR-assisted operations.
• Contribution: Built the AI healthcare platform, model/agent workflows, VR-glasses deployment platform, REST APIs, Next.js/React interfaces, and CI/CD pipelines.
• Impact: Delivered a production-ready AI product foundation that improved clinical document review, supported laboratory automation, and made VR fleet deployment manageable across environments.

Tech: TypeScript, Next.js, Node.js, React, Java, Spring Boot, Python, PyTorch, TensorFlow, Docker, PostgreSQL, OpenAPI, GitLab, GitHub Actions.

• Develop and demonstrate solutions using GenAI software like langchain, vercel ai sdk, copilotkit
• Work with customers to understand their challenges and provide the best solutions based on open-source data products
• Build RAG and GraphRAG solutions using Neo4j, lancedb, and Postgres
• Deploy a LLMOps platform using kubernetes, terraform, helmfile, Arize phoenix, mlflow
• Architect and build data pipelines using dbt, Trino, Spark, Iceberg, Airflow, ArgoCD, terraform, kubernetes
• Delivered user-centred technical strategy for Agriculture 4.0 and precision livestock farming, helping my client secure funding from Bpifrance
• Delivered a prospecting tool for a leading French solar carport installer, using geospatial computing (GIS), speeding up the sales process
• Built digital twin architecture for solar carports and EV chargers, making real-time monitoring and smart charging possible

• Proceeding what was started in 10/2023, offering data science development skills fulltime to international clients

• Helping companies learn more about their existing (unstructured) data, optimize processes and technical systems, and derive solutions for their problems

• Tools and technology used: Python (sklearn, pandas, numpy, Django, sqlAlchemy, pyTorch), Matlab, Docker, AWS EC2, Lambda, S3, SQL, MySQL, Hadoop & Spark, Machine Learning, DNN, AI, Jira, Confluence, Git, CI/CD, GitLab, Jenkins

• Optimized the data architecture (medallion) to better decouple processing stages and improve transparency and reproducibility
• Ensured technical quality of data processing in Databricks by introducing schema enforcement, data quality checks and a structured data architecture
• Orchestrated pipelines with Azure Data Factory
• Professionalized and automated the development and deployment process by integrating Git and GitHub Actions
• Led the Data Engineering team (3 members) in a functional role
• Conducted workshops to optimize and stabilize the data platform and the development process
• Collected and prioritized new requests, maintained the product backlog
• Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS SharePoint, Mural

• Supported the company's transition from an on-premise architecture to AWS cloud services, modernizing infrastructure and optimizing operational efficiency.
• Leveraged expertise in automation and software implementation to enhance scalability, reliability and profitability.
• Implemented Poetry and Ruff to streamline Python dependency management and code quality checks, improving development efficiency and reducing errors.
• Implemented an automated CI/CD strategy with GitHub Actions, which decreased deployment times and minimized manual intervention.
• Enforced deployment automation for Kubernetes, enhancing the scalability and reliability of applications across the organization.
• Evaluated and implemented Apache Airflow for workflow management, leading to more efficient scheduling and monitoring of data pipelines.
• Created data interfaces for energy traders, enabling them to optimize profit margins through improved data analysis and decision-making tools.

• Product strategy & vision: Led campaign performance reporting platform serving 700+ partners, transformed manual MSTR-based weekly reporting to real-time self-service platform enabling partner autonomy and operational efficiency
• Strategic roadmap management: Led phased migration prioritizing Performance campaigns (70% revenue) ahead of Awareness and Engagement, driving iterative platform evolution aligned with objectives, partner feedback, GDPR compliance, and data retention policies
• User research & customer discovery: Conducted regular user interviews with partners to understand reporting needs, decision-making processes, and additional KPI requirements, translating insights into platform enhancements and feature prioritization
• Cross-functional leadership: Collaborated with Product Consultants, analysts, data engineers, frontend teams, and product marketing to execute seamless platform migration, reducing PC team size by 2 FTEs while improving service quality
• Scaled user adoption: Strategically onboarded partners starting with top 30 partner-program partners, expanding to all 700+ partner-program and wholesale partners through user education documentation, training coordination, and iterative feedback incorporation
• Data-driven product optimization: Implemented Google Analytics tracking and engagement monitoring, identified low-engagement features (report downloads, detailed links), deployed AppCues and re-education campaigns resulting in 40% weekly engagement rate
• KPI standardization & governance: Led cross-functional initiative to standardize KPI definitions and formulas across reports, dashboards, and ZMS platform, defined North Star metrics and essential KPIs for each campaign objective ensuring consistent measurement and decision-making

• As a Data Specialist, I leveraged analytical expertise to transform raw data into actionable insights, driving strategic decision-making and operational improvements. My role encompassed data interpretation, reporting automation, and cross-functional collaboration, utilizing advanced tools such as Microsoft Excel, Power BI, and Python for comprehensive data analysis.
• Implemented Python scripts to validate and reconcile large datasets, reducing manual errors and improving data reliability.
• Utilized Python (Pandas, NumPy, Matplotlib/Seaborn) to automate data cleaning, analysis, and visualization, improving efficiency and accuracy in reporting.
• Developed interactive dashboards in Power BI to present key metrics, trends, and performance indicators, facilitating real-time decision-making.
• Designed and executed automated reports using Excel (Pivot Tables, Power Query, VBA) and Power BI, ensuring data accuracy and consistency across departments.
• Data Analysis: Excel (Advanced Pivot Tables, Power Query), Power BI (DAX, Data Modeling), Python (Pandas, NumPy, Visualization Libraries)
• Automation & Reporting: Power BI Dashboards, Excel Macros (VBA), Python Scripting.