Apache Airflow Experts in Munich

Mirza K.

Agentic AI for a DeepResearch project

Last position:

Agentic Automation and a RAG system

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

Philipp G.

Machine Learning & Data Engineer

Last position:

Data Scientist & ML Engineer at Data-Science Factory GmbH

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

Thomas H.

Senior MLOps, DevOps Engineer

Last position:

Senior MLOps, DevOps Engineer at Trianel Energy

Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
Develop and containerize PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), log centralization, and cost monitoring.
Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
Extend the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via MongoDB (Kubernetes) with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
Integration of RESTHeart to create a REST API for MongoDB.
Build an Angular frontend to simplify data queries and master data maintenance.
Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).

Suyash S.

Business Data Science Intern

Last position:

Data Analyst - Reporting & Analytics at SIXT SE

Developed & maintained customer analytical reporting solutions to identify revenue trends, performance drivers, risks & optimization opportunities to ensure data driven decision making across Sales, Finance, Product, Data Engineering & Controlling.
Defined & analyzed customer trends & performance metrics to identify root causes behind variances, anomalies & emerging risks across business domains to deliver actionable recommendations.
Developed & owned analytical data models & reporting layers to ensure scalability, performance & analytical robustness to support executive & operational reporting across business domains.
Planned, tracked & executed projects by ensuring adherence to timelines, data accuracy, consistency, deliverables, reliability & data quality standards through rigorous validation & reconciliation processes.
Raised the analytical maturity by formalizing analytical workflows, documenting data processes & standard operating procedures (SOPs) & conducting training sessions to drive adoption of self-service analytics & embed a data driven culture across operational and business teams.
Took ownership of the end-to-end lifecycle roadmap from requirement gathering, collection, transformation, developing robust business logics to data storytelling & stakeholder delivery.
Converted complexity into structured clarity by translating requirements & business processes into analytical recommendations to ensure alignment between non-technical & technical stakeholders.
Conducted advanced SQL based analysis of complex business datasets to uncover trends, correlations & performance improvement opportunities.
Drove process automation & efficiency improvements by leveraging Python, SQL optimization & AI assisted tools to reduce processing time & increase reliability across analytical & operational workflows.
Standardized KPI definitions & reporting logic to ensure consistency & trust across reporting solutions.
Developed process monitoring dashboards & analyses to identify inefficiencies, bottlenecks & compliance deviations across end-to-end business processes to derive actionable recommendations for process improvement & automation.

Any-Arlene N.

Data Analyst · SQL · Python · Tableau · Power BI

Last position:

Co-Founder · Data Engineering & Backend at zirikana (Kirundi Bible Web App) – Civic Technology

Built a Python pipeline that converts lectionary web content into structured daily JSON, applying liturgical-calendar rules for accurate weekday and Sunday coverage.
Shipped a read-only FastAPI REST API with shared Pydantic models and delivered a Kirundi-first web client for browser and mobile use.
Owned the data layer and backend architecture, collaborating closely on system architecture and interfaces while automating refreshes with GitHub Actions and validating the ETL with pytest.
Impact: Created a reliable, API-driven source of truth for daily Bible readings in Kirundi, enabling consistent access to previously unstructured content.

Serge K.

MLOps (machine learning operations)

Last position:

MLOps (machine learning operations) at REWE Digital GmbH

It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment

Eli R.

Senior Backend Engineer

Last position:

Technical co-founder at AskTheLaws

Create an AI legal assistant with modern ML capabilities.
Implement RAG architecture, with data pipelines for legal data search.
Use AWS Bedrock for LLM and embedding models and LangChain/LangGraph
Python with FastApi for backend and React for frontend

Michael T.

Senior DWH Developer

Last position:

ETL Developer at Insurance service provider

DWH for customer and financial data

Extension of the DWH with new data sources
Report development
Data quality management

Methodology: Scrum

Tools: Atlassian Confluence & Jira

Databases: Microsoft SQL Server

Programming languages: SQL, T-SQL

ETL: Microsoft SQL Server Integration Services (SSIS)

Frontend platform: PowerBI, Microsoft Reporting Services

Hardeep B.

Sr. Data Engineer

Last position:

Sr. Data Engineer at Charles Schwab Bank

Designed and implemented end-to-end data pipelines (batch & streaming) using Python, SQL, and Apache Spark, Databricks on AWS reducing ETL latency by 40%.
Developed serverless event-driven ingestion pipelines using AWS Lambda and SQS, ensuring real-time data availability for downstream analytics.
Leveraged Google Cloud Platform (GCP) services including BigQuery and Dataflow to manage cross-cloud data warehousing and analytics integration.
Expertise in DMS (CDC, Full Load) and Airflow for scalable data pipeline automation and orchestration.
Managed and customized data pipelines using Databricks, Airflow. Automation using Docker, Kubernetes, Terraform.
Automated data quality checks using dbt to modularize transformations and ensure production-grade data lineage, improving reliability by 30%.
Collaborated with compliance teams to ensure GDPR and SOC2 alignment. Mentored junior engineers and contributed to architecture refactoring for scalability.
Created and maintained dashboards in Power BI to provide actionable insights.

Sara Z.

Data Analyst / Analytics Engineer

Last position:

Data Analyst / Analytics Engineer at IDG Tech Media GmbH

Designed, built, and maintained scalable ETL/ELT data pipelines using Python, SQL, REST APIs, AWS Lambda, S3, PostgreSQL RDS, EventBridge, CloudWatch, Docker, Apache Airflow, and BigQuery – integrating data from GA4, Google Ads, Meta Ads, CMS, CRM, newsletters, events, and B2C ordering systems into analytics-ready datasets.
Built a cross-brand lakehouse architecture from AWS to BigQuery – transforming raw JSON/CSV data into structured, partitioned, and reusable reporting layers with staging, intermediate, canonical, and mart models.
Designed relational and dimensional data models: 3NF staging models, star schemas, fact tables, dimension tables, daily KPI aggregates, and dashboard-optimized marts for marketing, content, subscription, event, CRM, and revenue analysis.
Implemented production-grade data quality and pipeline reliability features: incremental loads, idempotent upserts, deduplication, schema validation, row matching, null checks, anomaly detection, freshness monitoring, logging, retries, and error alerts.
Automated cross-brand reporting processes and data products – pipelines for 73 newsletter campaigns, 31 lead list syncs, 52 event partner reports, and a 500K-record company matching pipeline; reduced manual data preparation by approx. 70% and increased analyst productivity by approx. 30%.

Christian S.

Data-Scientist/AI Engineer

Ismaning

Last position:

Data-Scientist/AI Engineer at The Marcom Engine GmbH & Co. KG

Concept creation and implementing AI Agents in AWS Cloud
Continuously alignment with stakeholders
Collaborate with DevOps
Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.

Nima N.

Data and AI architect

Last position:

Co founding LLM Engineer at LLM Ventures

Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
Designed and implemented multi-agent AI workflows for financial and trading applications
Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
Led system architecture decisions across model selection, orchestration, state management, and deployment

Stephan S.

Senior Data/ML Consultant & Technical Lead

Last position:

Senior Data/ML Consultant & Technical Lead at Jolin.io

Role: Software Engineer & Applied Mathematician (Mathematical optimization for scheduling; duration: 1 months; team setting: Team of 2, remote; technologies: JuMP, Julia, Pluto, Svelte, JavaScript, TypeScript, JetBrains Space, Terraform, Nomad)
Role: Software & Cloud & Web Engineer (Building scalable data science compute cluster from scratch; duration: 11 months; team setting: Team of 1, on-site; technologies: Terraform, Kubernetes, k8s ingress, k8s services, k8s RBAC, k8s networking, k3s, etcd, S3, DNS, certificates, Julia, Pluto, JavaScript, Tailwind, Astro, npm, Parcel, Preact, MUI, JWT, AWS SQS, AWS RDS, Python, GitLab, GitHub)
Role: AI & Web Engineer (Custom ChatGPT service; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Poetry, LangChain, Tailwind, ChatGPT API, Flask, FastAPI)
Role: Architect & Data Engineer (Central datalake setup and ingestion; duration: 9 months; team setting: Team of 5, remote; technologies: Infrastructure-as-code, AWS CDK, Python, Boto3, PySpark, AWS Glue, IAM, S3, ECS, Fargate, Lambda, Apache Hudi, DeltaLake, Databricks, GitHub, Jira, Miro)
Role: Software Engineer (PoC Julia migration of scikit-decide; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Julia, GitHub)

Nikolay T.

Senior Cloud Data Architect

Unterhaching

Last position:

Senior Cloud Data Architect at Cloudreach/Eviden (an ATOS Company)

Architected a self-service Google Kubernetes Engine (GKE) platform for a major financial institution (Commerzbank), enabling 1000+ users across hundreds of product teams to autonomously provision resources and significantly accelerate development cycles.
Designed a data-product-oriented platform architecture for the UK Department for Transport (DfT) to serve over 1500 direct end-users and numerous connected third-party systems, enhancing data accessibility and governance.
Drove business growth by developing the strategic roadmap for the 'One Cloud' business line, targeting a 10% revenue increase.
Served as a key member of the CTO Authority, providing strategic guidance on internal cloud initiatives and best practices.

Maziyar K.