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About

I build production software and the engineering practices needed to maintain it. As Engineering Lead at DQC, I lead engineering across the company and work directly on platform architecture and core product features. My role combines growing the engineering organization and guiding technical decisions with hands-on development of AI systems for enterprise data quality.

My background spans data science, quantitative finance and software engineering. Across those fields, my work has combined architectural decisions with direct implementation: building data systems, integrating models into products and making existing software easier to change safely.

Outside work, I enjoy trail running, chess, cooking and hiking.

Professional experience #

Engineering Lead — DQC (2024–present) #

  • Own technology strategy, engineering organization, and platform architecture for an AI-powered SaaS product while continuing to deliver core features in Python
  • Architect multi-layered agent and LLM workflows that combine machine-learning models, statistical systems, and data pipelines
  • Design the LLM interaction layer for intent routing, context and metadata enrichment, data-quality rule and workflow generation, and governed multi-provider access
  • Strengthened the backend and infrastructure of an existing product with batch and interactive workloads on Kubernetes, supported by ArgoCD, CI/CD, automated testing, and engineering standards
  • Built and scaled the engineering organization through hiring, onboarding, mentoring, knowledge transfer, and technical leadership
  • Drive the company-wide technology roadmap and tooling decisions with the CTO and Product
  • Led the migration from GitHub and Jira to GitLab and contributed to the platform’s move from Azure to AWS

Quantitative Developer & Data Engineer — 2Xideas (2022–2024) #

  • Built a backtesting engine covering multiple investment strategies, trading simulation, and performance analysis
  • Developed a centralized market-data repository with automated daily updates and data-quality checks
  • Built internal Python packages for quantitative research and turned research code into tested production software
  • Improved risk-modelling and asset-management products
  • Added and maintained external data integrations through REST and FTP, including S&P Capital IQ, MSCI, and Trading Economics
  • Modernized legacy code, updated core dependencies, and expanded automated test coverage
  • Supported hiring through technical interviews, job postings, and candidate selection

Data Scientist — anacision (2020–2022) #

  • Built a web application and optimization algorithm for pharmaceutical personnel planning
  • Implemented and tuned different GANs to generate differentially private synthetic data
  • Developed automotive predictive-maintenance models using terabytes of data with PySpark and Azure Databricks
  • Maintained and expanded a REST API for smart-meter data using Azure, Databricks, and Flask
  • Contributed to a prediction-as-a-service platform using Azure, PostgreSQL, Redis, and GitLab
  • Maintained internal business software built with Django
  • Created project-scaffolding tools and a shared design package for consistent development and deployment
  • Administered GitLab and established software-development practices for data-science teams

Business Consultant — Financial AI — msg for banking (2018–2020) #

  • Forecasted trading volumes and prices of financial derivatives using market, news, and social-media data
  • Backtested and benchmarked financial models using multivariate time-series analysis
  • Performed customer-retention, churn, segmentation, and data-quality analyses
  • Used predictive analytics to help banks identify potential customers for loans, credit cards, and investment products
  • Worked on market-risk and regulatory topics including model validation, IRRBB, liquidity risk, stress testing, and risk-governance frameworks

Technical depth #

  • Python & backend: Python · Pydantic · FastAPI · SQLAlchemy · PostgreSQL
  • AI: PydanticAI · DSPy · LLM gateways · Multi-provider routing
  • Data: Polars · pandas · PySpark · Ibis · Arrow
  • Platform: AWS · Azure · Kubernetes · Docker · GitLab CI/CD · ArgoCD
  • Verification & observability: pytest · Testcontainers · Dash0 · Sentry · Grafana

Further experience #

Research and Teaching Assistant — Otto-Friedrich-University Bamberg (2016–2018) #

  • Researched time-series analysis, stochastic-volatility models, GARCH, and option pricing in relation to financial anomalies and exogenous shocks
  • Organized and taught lectures and exams and supervised five bachelor’s and seven master’s theses

Conference Manager — Swiss Society for Financial Market Research (2017–2018) #

  • Organized the SGF Conference 2018 in Zurich with more than 150 guests and 300 research contributions
  • Coordinated 91 presentations across seven conference rooms at SIX Swiss Exchange ConventionPoint

Education #

See also: Links