
Apache Hadoop Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Apache Hadoop
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Mirza K.
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
Florian B.
Last position:
Project Management / PMO Consultant
PMO & Project Organization Blueprint for Restructuring
Renewable Energy / Solar Equipment
Technologies / Methods: PMO setup, KPI tracking, project organization, Jira, Confluence
- Developed measures to improve management control during a restruct...
Philipp G.
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
Valery K.
Last position:
Sr. Data Scientist & Engineer at Virtual Minds
- Development High-Performance ad distribution via auction
- Holistic (multi-campaign & multi-channel) advertisement placement optimization
- Algorithmic optimization for NP-Hard/NP-e
- Multiple Knapsack Problem with constraints
- Online estimation of parameters in stochastic environments
Tools: Python, R, Kotlin, MILP/SAT/CP Solvers, Pytorch, Pandas, Docker
Serge K.
Last position:
Data Scientist & 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
- Development of feature store and data synchronization between environments
- Data science: evaluation of different forecasting models, including CatBoost, Contextual Gamma and Random Forest; implementation of training pipelines using Kubeflow on Vertex AI; scaling and development of an event-driven interface using Kafka as a forecasting service; evaluation of MLFlow for model tracking and model registry
- Implementation of REST API using KeyCloak OAuth2 and OpenID (JWT)
- Monitoring: implementation of dashboards with Grafana, Prometheus, Mimir, Streamlit and Loki to monitor prediction metrics, GCP and Snowflake costs for pipelines, and system metrics
- Development of Copilot Agent Skills
Christiane N.
Last position:
Management Consultant at Christiane Neher Management Consulting
Large Insurance Company – Consultant Wiesbaden: Consulting support for the introduction of an integrated planning and performance management framework (operational, financial, customer) to enhance customer-centric transparency, decision-making quality, and steering capabilities across all lines of business within an insurance organization:
- Analysis of existing processes, reports, KPIs, and KPI calculation methodologies
- Design and introduction of new, standardized customer KPIs (gross/net), as well as key steering metrics with consistent linkage across all lines of business
- Recalculation, validation, and plausibility checks of KPIs based on existing and newly integrated data sources
- Conceptual support for the development of an integrated reporting and performance management setup
- Execution of customer insights analyses to identify patterns and anomalies within customer data clusters
Large retail company – Consultant in Karlsruhe: Advisory services for the setup and step-by-step implementation of an internationally deployable RELEX solution in the supply chain management environment:
- Advising overall and sub-project management on methodology, project setup and steering (e.g. agile approach, Jira configuration, RELEX phases, Jira Structure PPM)
- Strategic-operational consulting for the introduction of RELEX including best practices
- Support in defining overarching goals and requirements (2-year target picture)
- Guidance in scoping a relevant supply chain network segment for the project
- Development of a roadmap for iterative, incremental RELEX setup and rollout
- Assessment of project dependencies (interfaces, configurations, etc.)
- Advice on prioritized implementation of business requirements and data interfaces
- Support in test planning (data validation, system testing, UAT)
- Consulting on internationalization, change management, training, and knowledge transfer
- Stakeholder advisory and alignment activities between the client, implementation partner, and RELEX
Insurance company – Management Consultant in Munich: Analysis, consulting and support for the optimization of a large-scale business and IT transformation. Focus on strategically important programs and modernization projects in the area of Managed Services Operations and processes:
- Review of project plans and deliverables; analysis of programs and projects (e.g. cloud approach, process standardization, system integration, roadmaps)
- Identification of technical, functional and personnel risks and challenges; development of content-related measures and alternative solutions
- Proposal of quality improvements for program and modernization efforts
- Sparring partner and professional, technical, structural and organizational consulting for project and program management
Large retail group – Management Consultant & Stream Lead in Cologne: Consulting, process, project and product management for the introduction and implementation of a large strategic program in the field of advanced analytics, assortment and space management:
- Setup, test and rollout of a new space planning, automation and optimization product based on the existing cluster-based merchandising approach
- Definition and setup of new processes and transformation and change management measures for the new store-specific merchandising approach
- Collaboration with Advanced Analytics and IT (internal and external) for software implementations, automations, extensions and interfaces
- MVP approach and piloting in phases with gradual rollout (pilot with 80 stores, region with 500 stores, national level with 4000 stores)
Large retail company – Agile Coach & Change Agent in Cologne: Agile coach, OKR master and facilitator for the introduction of the OKR approach in a large strategic digitization program for retail stores:
- Coaching of the core team with topic managers and team leads
- Introduction to the OKR topic and setup of the OKR cycle
- Establishment of the OKR approach in teams and on a cross-team level
Delivery and logistics company – Management Consultant in United Kingdom: Consulting and coaching in the restructuring of the Data Analytics department:
- Analysis of current challenges
- Definition of overarching goals
- Development of a proposal for a new team structure
- Identification of required competencies, skills and responsibilities
- Advisory and alignment on communication and change management strategy
Christian S.
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.
Anton K.
Last position:
Head of Overall Technical Integration NSC / Hadoop Cloud Development at IABG
Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).
Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.
Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management lifecycle.
Kubernetes, OpenStack and Hadoop are used as the foundation.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.
Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.
Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, DB.
Technologies: Kubernetes (K3s, Rke2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3S), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).
Stephan S.
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)
Chaitanya Kumar D.
Last position:
Data Science Consultant at Volkswagen AG
- Designed and deployed GDPR-compliant data pipelines.
- Developed machine learning algorithms for after-sales analysis, improving repair detection.
- Built cloud-based data lake architecture, enabling cross-functional digital transformation.
Maziyar K.
Last position:
Data Engineer at MSD Germany
- Lead Architect to design and implement the data lake and ETL Pipeline using AWS Stack
- Performance Optimization of Data Ingestion of ETL Pipeline
- Development of Data Validation using Great Expectations
- Leading of the data migration for two sources exchanges
- Data Modeling in AWS Redshift
MLOps
- Model inference implementation by mlflow and AWS SageMaker
- Feature Engineering for the running ML Models ( Recommender Engineer, Clustering )
- Implementatino of Model Registry and artifactory using mlflow
- Historization an Profiling of the Input Data Using AWS Glue Crawler and AWS Data Catalog
- Feature importance using mlflow
Tech. Stack: Python 3, AWS Glue, AWS Step Fucntion, AWS Lambda, AWS EventBridge, AWS IAM Role, AWS SageMaker, AWS EC2, AWS Glue Crawler, AWS CloudWatch, MLFlow, ETL, Data lake, GitHub Action, Terraform, Jenkins, Ansible playbooks (Infrastructure as Code), CI/CD, GitLab, SQL, PySparkSCRUM, Agile, Jira, BigData, VSCode, DBeaver, MSSQL, MySQL, grafana, Docker, Linux, Bash, MapReduce, Data Modeling (ORM), Pandas, YAML, SQL-Alchemy
Biju K.
Last position:
Freelance AI Strategist & Governance Expert at DataSiens Freelancer
- Developed the AI strategy for a major Austrian retailer with over €10 billion in annual revenue.
- Developed a go-to-market strategy for AI services for a Norwegian consulting firm specializing in SAP technologies.
- Delivered AI for Business training programs to a leading German supermarket chain.
- Defined AI governance project structure and roadmap for a large German manufacturer.
- Certified facilitator for AI Design Sprint™, leading use case discovery workshops for large enterprises.
- IEEE Certified AI Ethics Assessor with expertise in building AI governance frameworks aligned with the EU AI Act.
- Founder of aiethicsassessor.com as knowledge base for AI governance and AI legislation.
- Author of a best-selling Udemy course on Data Architecture.
- Developed intelligent agents using low-code/no-code platforms to automate complex business processes.
Eyasu H.
Last position:
Data Scientist at Deutsche Bundesbank
- Developed web scraping scripts to extract and parse over 5000 product data from the Zalando website.
- Performed ETL processes using Apache Spark in CDSW, loaded the data into the Hadoop ecosystem (HDFS), and managed data using Hive and Impala.
- Implemented machine learning algorithms, achieving 85–90% accuracy on multi-class product classification.
- Integrated Zalando's product and price data into the dashboard with Otto and Takko for interactive visuals.
Josef S.
Last position:
DevOps at Software house for an industrial company
- Implementation, maintenance and operation of an ERP system and a document exchange platform for a corrugated cardboard manufacturer.
- Tools and systems: Unix (Debian 6.x), C, SVN, Windows, C#, MS SQL Server, SCRUM.
Discover over 15,000 top freelancers
Statistics of experts using Apache Hadoop
Aggregated from the professional profiles of matched freelancers.
Experience
21 years (Germany: 18 years)

Position duration
2 years (Germany: 2.8 years)

Positions per freelancer
17 (Germany: 12)

Top business areas
Business Intelligence, Information Technology, Product Development

Top industries
Information Technology, Banking and Finance, Professional Services

Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100% (Germany: 95%)
Master's degree or higher
71% (Germany: 65%)
Doctorate
14% (Germany: 10%)

Certifications per freelancer
3 (Germany: 4)

Most common languages
German, English, Spanish

Speak two or more languages
100% (Germany: 97%)
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Munich using Apache Hadoop
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Apache Hadoop experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (93%)
- Banking and Finance (80%)
- Professional Services (67%)
- Automotive (60%)
- Education (53%)
- Manufacturing (53%)
- Retail (53%)
- Insurance (47%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Distributed data foundations
Apache Hadoop is an open-source framework for storing and processing very large datasets across clusters of standard servers. Its core components, HDFS and YARN, distribute files and compute workloads while supporting fault tolerance. Companies use Hadoop for data lakes, batch analytics, log processing and large-scale ETL.
Core ecosystem
Hadoop work rarely stops at the core framework. Specialists work with Hive for SQL-based querying, MapReduce for batch computation and Sqoop or Flume for data ingestion. Spark is often paired with Hadoop for faster analytics, while Oozie, HBase, Avro, Parquet and Kerberos support workflow management, storage formats, databases and security.
Typical delivery work
- Design HDFS layouts, cluster capacity and data-retention policies
- Build ingestion and transformation pipelines for logs, events and operational data
- Develop Hive, Spark or MapReduce workloads for reporting and analysis
- Connect Hadoop data with warehouses, BI tools and machine learning workflows
When specialists help
Companies bring in freelance Hadoop specialists during data-platform migrations, cluster redesigns and performance investigations. They can modernize legacy MapReduce workloads, improve data quality or move selected processing to cloud services without disrupting critical pipelines. Munich teams may benefit from local on-site workshops combined with remote delivery, depending on security and collaboration needs.
Skills that matter
Strong professionals understand distributed storage, partitioning, replication, resource scheduling and failure recovery. They also bring practical Linux, Bash, Java, Python, SQL and data-engineering skills. Experience with cloud Hadoop services, containerized workloads, monitoring, access controls and governance helps when a cluster must connect to a broader enterprise architecture.
Choosing the right expert
Look for evidence of production work with cluster operations, workload tuning and dependable data delivery, not only familiarity with Hadoop terms. Ask how the specialist handled node failures, skewed data, schema changes, access controls and rising workload demand. A good expert explains trade-offs clearly, documents operational decisions and leaves behind tests, runbooks and maintainable pipelines.
Frequently asked questions
Need clarity? These are the questions we hear most often about Apache Hadoop.
Apache Hadoop is used to store and process large datasets across distributed clusters. Companies apply it to data lakes, batch ETL, log analysis, reporting and historical data workloads that are too broad or costly for a single server.
Apache Hadoop provides a broader storage and resource-management foundation through HDFS and YARN, while Spark is a processing engine designed for fast batch, streaming and analytical workloads. They can work together, although a project may use Spark with cloud storage instead of a traditional Hadoop cluster.
A strong Hadoop specialist usually knows Hive, Spark, SQL, Java or Python, Linux and distributed-system fundamentals. Useful additional skills include Kafka, cloud data services, Parquet, workflow orchestration, monitoring and data-security controls.
The right level depends on the work: a focused Hive pipeline needs less operational depth than a cluster migration or production redesign. For critical workloads, choose an Apache Hadoop professional who can show experience with capacity planning, failure recovery, security and performance tuning in comparable environments.
Yes. Apache Hadoop work is often suitable for remote collaboration when secure access, documentation and clear ownership are in place. On-site sessions in Munich can still help with architecture workshops, stakeholder alignment or restricted infrastructure, while English is commonly used in technical delivery.
Apache Hadoop can remain suitable when a company needs control over infrastructure, existing cluster investments or predictable processing on large internal datasets. Cloud-native services may be preferable when elastic capacity, managed operations and reduced infrastructure ownership matter more than cluster control.
Ask the Hadoop professional to explain a real cluster decision, including data layout, resource allocation, monitoring and recovery procedures. Strong answers connect technical choices to reliability, cost, security and maintainability rather than listing tools alone.
Work with Apache Hadoop often includes legacy modernization, ingestion design, query tuning, platform operations and integration with Spark or cloud storage. Specialists may need to balance delivery with documentation, access governance and careful migration planning because Hadoop data frequently supports important business processes.
The average hourly rate of freelancers in Munich, Germany who have used Apache Hadoop in their recent projects is 107 €, which corresponds to a daily rate of about 854 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Apache Hadoop in their recent projects, 100% hold at least a Bachelor's degree, 71% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Munich, Germany who have used Apache Hadoop in their recent projects have 21 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Munich, Germany who have used Apache Hadoop in their recent projects are German (100%), English (100%), and Spanish (27%).
The most common industries among freelancers in Munich, Germany who have used Apache Hadoop in their recent projects are Information Technology (93%), Banking and Finance (80%), and Professional Services (67%).
The most common business areas among freelancers in Munich, Germany who have used Apache Hadoop in their recent projects are Business Intelligence (100%), Information Technology (93%), and Product Development (80%).
Main locations of FRATCH Experts, who have recently used Apache Hadoop
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
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