Senior Python Developer - Full-Stack - Data / Analytics / AI Solutions

Senior Python solution architect & developer. On-prem, cloud-native, hybrid on AZ, GCP, AWS. Focus on data-/analytics-/AI-driven applications, backends, libraries and full-stack-apps. Strong background in latest data technologies, ML, AI and LLMs.

• API-Entwickler
• Backend Entwickler
• Cloud (allg.)
• Container Management
• Data Science
• Data Warehousing
• Large Language Models
• Python
• Software Architecture7 J.
• Softwareentwicklung (allg.)30 J.

Senior Python architect, developer, project lead, coach and advisory for analytical applications. Full scope from non-technical business requirements up to auto-scaling operations. All project size and complexity levels, incl. application modernization projects.

Focus on data- and AI-driven analytical applications, auto-scalable backends (if required), web-services and APIs, custom libraries and full stack applications. Lastest Python stack, if required, extended with C or C++. If possible, utilization of containerized deployments, automated build - and processing pipelines.

Expert (Fellow and Senior Analyst at BARC, Würzburg) for modern data architectures esp. Data Lakehouse (Iceberg, Delta, Hudi) and data stacks like Databricks, Snowflake, MS Fabric, Starburst, Dremio and many others.

Expert in data analysis and data science (6 years acting professorship for business analytics and data science at HSD University Düsseldorf). Experience from 100+ (no joke) data science project, ranging from classic OR and statistics, over ML and NLP up to latest LLM and agent based systems.

Developer or co-developer of several open (and closed) source projects in the data and analytics space.

Many references over almost all industries. Ability and broad experience to talk to stakeholders over all management levels.

If required, access to top-level nearshore resources (up to 50 FTE) for scale in larger projects or cost-efficient maintenance.

Languages:
- primary: Python
- secondary (to extend Python): C, C++ or Rust
- others: TypeScript, JavaScript, C#, Java etc.
- database: SQL, T-SQL, P-SQL, MDX, DAX, Cypher etc.

Architecture:
- backend: FastAPI, flask, Django, Node, serverless (on-prem & cloud) etc.
- frontend: Web, QT, tkinter, Anvil, Reflex, JustPy, Dash etc.
- environment: Docker, Podman, Kubernetes, Note: Access to top-level Kubernetes experts for high-complex setups.

Data:
- classic: Oracle, MS-SQL, PostgreSQL, MySQL, Maria, Mongo, Redis, HANA etc.
- modern: Iceberg, Delta, Trino, file-based (parquet) etc.
- graph: neo4j, ArangoDB, TigerGraph, PostgreSQL
- cloud: Big Query, ADLS, Athena, Databricks, Snowflake, Redshift etc.
- Python: Spark, SQLite, DuckDB, Arrow, Pandas, Numpy, PolaRS etc.

Analytic, ML, AI:
- whatever is required and of advantage...
- primarily: numpy, SciPy, Scikit-learn, Tensor-Flow, Keras, Prophet, XG-Boost etc.
- visualization: Matplotlib, seaborn, plotly, plotly-dash, D3js etc.
- LLM: models: OpenAI, Gemini, Llama, Mistral, Claude, DeepSeek etc., tooling: n8n, LangGraph/LangChain, HuggingFace, Rivet etc., approach: Agents, RAG, CAG, fine tuning etc.