Apache Hadoop Experts in Frankfurt
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Meet FRATCH Experts in Frankfurt, who have recently used Apache Hadoop
Umut G.
Last position:
Data Architect at BA Technology
I am an experienced data engineer specializing in end‑to‑end data integration, cloud DWH architectures, and high‑quality, governed data products.
I delivered following projects and engagements as a freelancer.
- Data Migration of CRM System for AL-FA Objekt Service Gmbh
- Microsoft Software Resales Partnership
I am looking for freelance roles like: Freelance Data Engineer Cloud Data Warehouse Architect Data Modeling & Architecture Consultant MDM & Data Governance Specialist BI & Analytics Developer
Technical Focus Areas
- Data Engineering & Integration: SQL Server/SSIS, Informatica PowerCenter/IDQ, Talend, Kafka, Azure Data Factory – Delta/CDC/ELT patterns, robust pipelines, monitoring/recovery, data lineage & impact analysis, medallion architecture Bronze/Silver/Gold layers
- DWH & Cloud: Azure SQL / Data Lake / Synapse, AWS Redshift/S3, on‑prem SQL/Oracle – scalable data marts with a strong cost/benefit focus.
- Data Modeling: Atomic (Inmon) and Dimensional (Kimball), Data Vault (Linstedt), Domain‑Driven Design, clear lineage & contracts.
- MDM & Governance: Informatica MDM, IBM MDM, stewardship processes, data quality rules, survivorship/XREF, catalog/glossary, SIF/BES/REST publication.
- Analytics/BI: Power BI, SSAS, Cognos – business‑ready, maintainable data products.
Prasad T.
Last position:
Solution Architect / Senior Manager – DTC E-Commerce Platform at BRITA
- Led discovery phase and POC for Shopware to Shopify Plus migration across EMEA markets, evaluating platform suitability, technical architecture, and multi-brand/multi-country capabilities against business requirements.
- Designed reference architecture for Shopify Plus implementation incorporating headless front-end patterns (Vue.js, Nuxt.js), CMS integration (Magnolia), and Azure middleware (APIM, Functions, Logic Apps, Service Bus) for 11 EMEA markets.
- Defined migration strategy analyzing data mapping, cutover approach, and zero-downtime deployment patterns using Varnish caching, GitOps pipelines, and CI/CD orchestration across six vendor teams.
- Architected multi-tenant Shopify Plus governance model with centralized admin, localized storefront customization, and compliance controls (GDPR, data residency).
- Prototyped AI-driven search optimization (LLM.txt, JSON-LD) for product discoverability in Google AI results, demonstrating post-launch performance opportunities.
- Defined EMEA expansion roadmap for 15+ markets through C-level strategic workshops, identifying phased rollout, market-specific configurations, and resource requirements.
- Tech Stack: React, Nuxt.js, Vue.js, Magnolia CMS, Shopware, Shopify Plus, Azure (APIM, Functions, Logic Apps, Service Bus, Front Door), Varnish, SAP, MS Dynamics, Docker, Kubernetes, GitHub Actions, PostgreSQL, Kafka
Bardiya B.
Last position:
Data Scientist at Rewe Digital GmbH
Statistical Forecasting Algorithm
- Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
- Migration from R/On-premise to Python/Snowflake
- Productionalization on Snowflake in cooperation with data engineers & DevOps
Monitoring Dashboard
- Data engineering for preparation & provisioning of necessary data/resources on Snowflake
- Development & deployment of a Streamlit dashboard in Snowflake
ML-based Probabilistic Forecasting on Vertex AI
- Development of a ML-based probabilistic forecasting algorithm from scratch
- Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI
Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow
Ulm P.
Last position:
DataStage ETL Expert at ING Bank
- Datastage 11.7, dbt, Oracle 19, Python 3.12 / PySpark 3.5, Azure GitHub, Azure DevOps, Automic
- Development of migration jobs to transfer data from the collection DWH to the new Risk Mart, as well as development of ETL pipelines to migrate historical data from the old Mart to the new Risk Mart.
- Storage of the silver layer on Hadoop and the gold layer in Oracle.
- Translation of DataStage jobs into dbt to publish reporting data in Google Cloud to a PostgreSQL database.
- Creation and optimization of complex SQL queries for data extraction from a data vault, taking into account historical data in the point-in-time tables.
- Creation of Oracle table definitions (DDL) and adjustment of existing stored procedures.
- Versioning changes in GitHub and deployment via the CI/CD portal.
- Refactoring long-running DataStage jobs into Python using PySpark to reduce server load.
- Migration of SAS scripts to PL/SQL, including new development of distribution functions that have no direct equivalent in Oracle.
- Development of Automic jobs to run DataStage pipelines and Python scripts (PySpark jobs) that control the population of the SME and institutional risk tables in the Risk Mart and perform business calculations.
- Participation in the agile process, including creating user stories, estimations, and planning in Azure DevOps.
- Handling Azure DevOps tickets and close collaboration with testers and business teams for error analysis and resolution.
Ashkan Z.
Last position:
Microsoft Azure Senior Data Engineer / Senior Data Scientist at Vattenfall Europe
- Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
- Independently designing analytics solutions with Python, SQL, etc.
- Designing and implementing ETLs and data pipelines
- Creating and maintaining APIs
- Independently applying CI/CD, testing, and version control
- Data modeling
- Model development and optimization
- Anomaly detection with AI
- Predictive analytics
Used technologies:
- Snowflake
- Fabric
- Azure Synapse Analytics
- Azure DataFactory
- Azure Data Lake
- Azure DevOps
- Databricks
- Spark
- CI/CD
- SQL Database
- Python
- Power Platform
Alona L.
Last position:
AI Architect
AI-powered platform for automated UX validation and designer support
- Designed and led technical implementation of an enterprise-wide AI solution for automated UX review that improved design quality and significantly reduced manual review processes in teams
- Developed an automated UX validation tool as a Figma plugin and web application that generates test cases based on internal guidelines and reliably checks current designs for consistency and standard compliance
- Implemented an interactive designer chat based on RAG that answers questions about the current design and the company's UX guidelines, and designed the deployment architecture using containerized services
- Python, Azure OpenAI, PostgreSQL, REST API, Docker, OpenShift, Helm, CI/CD, Figma MCP, LLM, RAG, Prompt Engineering, GenAI, XAI, AI Architecture, AI Strategy
Ahsan J.
Last position:
Data Analytics Developer at Level Next Productions
- Built Power BI dashboards and enabled data-driven strategies across digital platforms
Tan P.
Last position:
DevOps Engineer in the DevOps Team at Rise-World
- Implementation of specified DevOps solutions to automate infrastructure (Terraform, Bicep, CloudFormation, Ansible) on-premises datacenter (Ovirt, Proxmox, Ceph Cluster, MinIO) and private cloud.
- Administration, configuration and implementation of CI/CD DevOps pipelines (GitLab, GitFlow) to support development process (Artifactory, Prometheus, Istio, service mesh, Helm Chart, OpenShift (Red Hat Enterprise) / Kubernetes cluster), Red Hat Satellite.
- Administration, setup, monitoring and patching of Linux infrastructure based on Red Hat Enterprise for Dev, Test and QA.
- Use of Scrum and Kanban methods.
- Administration, configuration and implementation of security standards for deploying on Dev, Test, QA and Prod stages of the new ePA applications.
- Development of new plugins and add-ons needed on current infrastructure.
- Database support.
- Data analytics support (Python, Spark, Pandas, Power BI, Splunk Enterprise).
- Implementation of best practices for DevSecOps and BizDevOps using GitOps (ArgoCD), Streamlit framework, Semaphore Ansible UI.
- Configuration and testing of iperf, uperf, sysbench using benchmark-operator for external source data and IoT/MDM devices, creating reports via ELK / OpenSearch.
- Building a new Databricks platform to collect and analyze big data from different sources and IoT devices into Hadoop framework (Python, Pandas, PySpark, Power BI, Apache Airflow).
- Building backend data aggregation and processing to automate configuration deployment between different OpenShift clusters and big data framework (Python, Pandas, PySpark, Apache Spark, PostgreSQL, Django 2, Ansible Automation, Jira JSM).
- Building a new ML pipeline platform using Kubeflow, TensorFlow, KServe.
- Data extraction, transformation and loading from different data sources including structured and unstructured data to analytic DWH / big data cluster using Python, Pandas, Polars, Power BI, Django backend and PostgreSQL.
- Setup of new DevOps Test and QA HashiCorp Vault cluster for PKI and IAM.
- Configuration and testing of automated patching based on CVSS score, SIEM-integrated CVEs.
- Use of Nexpose and InsightVM to scan vulnerability events in network, host, container and application.
- Design and implementation of secure and scalable AWS architectures including VPC, EC2, S3, RDS and Route53 and similar setups on Azure and GCP.
- Automated system provisioning and deployment using CloudFormation templates.
- Configuration of IAM roles, policies and permissions to ensure secure access control.
- Patch management, backup automation and disaster recovery setup on AWS infrastructure.
- Monitoring and optimization of system performance using AWS CloudWatch and AWS Trusted Advisor.
- Support of VMware services (vSphere, Aria, Horizon) and the virtual desktop environment.
- Development and maintenance of CI/CD pipelines using Jenkins, GitLab CI/CD and AWS CodePipeline with interface to Nutanix.
- Configuration of AWS CloudWatch to monitor application performance and system events.
- Planning and execution of migration of on-premises applications to AWS cloud platforms.
- Deployment of containerized applications using Docker and Kubernetes in AWS environments.
- Deployment of internal software packages between availability zones using AWS CodeDeploy.
- Building and deploying ML models using Scikit-learn, XGBoost and Spark MLlib including hyperparameter tuning, model evaluation and production deployment.
Yevgeniy Ă–.
Last position:
Tester, Test & Data Analyst at NORD/LB
- Analyze system requirements and mapping concepts (ETL requirements) for data flows and transformation logic in the bank's DWH
- Analyze data in DB tables and views of the DWH using SQL (DB2)
- Independently define, create, and execute test cases in JIRA Xray (SIT)
- Write SQL queries in DB2 to verify data scenarios and mappings
- Create test plans for SAP FSDP and concurrent projects
- Conduct error and root cause analyses in coordination with business analysts, developers, test managers, and the infrastructure team
- Thoroughly document test results in JIRA Xray
- Coordinate between business analysis, development, DB infrastructure, business units, and external vendors
Ritika S.
Last position:
AWmOpsRtKekEX(CPEliRenIEtN: CInEfoSrs.yDs,aHtaitAarcchhiiEtencetr(gAyW) S)
Global marketing analytics for Hitachi Energy as part of a global data modernization initiative aiming to enhance data retention, historical data availability and provide Eloqua's 2-year retention for remote interaction reporting and analytics.
Analyzed Eloqua's default retention policy and identified risk of data loss for records older than two years.
Designed and implemented historical data preservation strategy by creating transformed tables in the target data platform to archive older data while ensuring data quality dashboards.
Collaborated with the Power BI team to re-point dashboards from raw Eloqua imports to the newly created archival layer.
Leveraged Jira to track and manage data engineering tasks, bugs, and feature requests across Agile sprints; coordinated backlog prioritization and task assignment to align data pipeline development with business needs.
Power BI dashboard optimization:
Worked closely with business stakeholders to assess and understand reporting needs for reverse customer data.
Designed and implemented incremental refresh in Power BI to ensure daily updates without full data reloads.
Collaborated with Azure data engineers to optimize data processing and publication pipelines.
Stakeholder communication & data modeling:
Acted as liaison between Group Data Office and Technology Office to align data modelling standards.
Gathered requirements from data engineering team and participated in weekly status meetings to provide implementation updates and resolve blockers across teams in Germany, Poland, and India.
Documentation & quality assurance:
Prepared end-to-end technical design documentation, data flow diagrams, and Power BI audit guides for future reference.
Participated in UAT sessions with business users to validate data outputs and report accuracy.
Discover over 15,000 top freelancers
Statistics of experts using Apache Hadoop
Aggregated from the professional profiles of matched freelancers.
Experience
19 years (Germany: 18 years)
Position duration
1.6 years (Germany: 2.8 years)
Positions per freelancer
14 (Germany: 12)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Healthcare
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100% (Germany: 95%)
Master's degree or higher
70% (Germany: 65%)
Doctorate
20% (Germany: 10%)
Certifications per freelancer
8 (Germany: 4)
Most common languages
German, English, French
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 Frankfurt 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 Frankfurt 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 (90%)
- Banking and Finance (70%)
- Healthcare (60%)
- Retail (50%)
- Energy (40%)
- Insurance (40%)
- Professional Services (40%)
- Transportation (30%)
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, including HDFS and YARN, distribute data and computing work while supporting fault tolerance. Companies use Hadoop for data lakes, batch analytics, log processing and large-scale transformation pipelines.
Core ecosystem
Hadoop work commonly includes MapReduce, Hive, HBase and Sqoop, alongside Apache Spark for faster analytics and iterative workloads. Strong specialists understand cluster resource management, storage layouts, schema design and data movement. They also work with Kafka, Airflow, Trino, cloud object storage and containerized environments when Hadoop forms part of a wider platform.
- Design HDFS storage and YARN capacity plans
- Build Hive, Spark and MapReduce processing jobs
- Connect ingestion, scheduling and governance tools
- Tune partitioning, serialization and resource queues
Practical use cases
Hadoop supports clickstream analysis, recommendation data, risk modelling, telecom records, scientific research and industrial telemetry. It is useful when data arrives in many formats and must be retained for repeated analysis. Teams can combine structured warehouse data with raw files, events and machine-generated records in one governed environment.
- Create batch pipelines for data lake ingestion
- Consolidate logs, events and operational records
- Prepare datasets for reporting and machine learning
- Migrate legacy processing into distributed storage
When specialists help
Companies bring in freelance Hadoop expertise during platform migrations, cluster redesigns, performance investigations and major data integration work. Specialists can also help when internal teams inherit an aging environment, face unstable jobs or need to connect on-premises storage with cloud services. In Frankfurt, remote collaboration is common, while some regulated or operational environments may require on-site workshops.
Skills that matter
A capable professional combines Hadoop knowledge with Linux operations, Java or Scala, SQL and distributed-systems fundamentals. Security work may include Kerberos, Ranger, encryption, access controls and audit trails. Experience with cloud services, infrastructure automation, monitoring and data governance helps keep the platform reliable beyond the first release.
Judging quality
Look for evidence of production Hadoop clusters, not only course knowledge. Ask how the professional handled data skew, failed nodes, slow queries, capacity limits and recovery procedures. Strong specialists explain trade-offs between HDFS, cloud storage, Spark and warehouse technologies, document operational decisions and make pipelines observable, testable and maintainable.
Frequently asked questions
Key details about Apache Hadoop, drawn from the questions we get asked most.
Apache Hadoop is used to store and process large datasets across clusters of servers. Companies use its HDFS and YARN components for data lakes, batch processing, log analysis, data integration and other workloads that exceed the practical limits of a single machine.
Apache Hadoop provides distributed storage and resource management through components such as HDFS and YARN, while Apache Spark focuses on fast, general-purpose data processing. They can work together, although a project may choose Spark with cloud object storage instead of a traditional Hadoop cluster.
A strong Hadoop professional often brings SQL, Java or Scala, Linux operations and distributed-systems knowledge. Useful adjacent skills include Apache Spark, Hive, Kafka, Airflow, cloud storage, Kubernetes, security controls and data governance.
The required experience depends on the work. A simple Hive pipeline needs different knowledge from a secure, multi-tenant cluster migration. For production work, choose a Hadoop specialist who can show practical decisions around capacity, data recovery, performance, monitoring and operational ownership.
Yes. Apache Hadoop work is often suitable for remote collaboration through shared repositories, documentation, monitoring access and scheduled technical sessions. On-site work in Frankfurt can still help with workshops, restricted environments, hardware-linked clusters or close coordination with local data and operations teams.
Hadoop can suit organizations that need control over distributed storage, varied file formats, large batch workloads or on-premises data processing. A cloud data warehouse may be simpler for managed analytics, so the decision should consider governance, latency, operating effort, existing skills and the required query patterns.
Ask the professional to explain a real cluster incident or pipeline redesign in concrete terms. A reliable Hadoop specialist should discuss data skew, failed nodes, partitioning, resource queues, security, observability and recovery rather than describing only framework features.
Apache Hadoop commonly refers to an ecosystem built around HDFS, YARN, MapReduce and related tools. Projects may also use Hive, HBase, Oozie or integrations with Spark, Kafka and cloud storage, depending on whether the priority is batch processing, ingestion, analytics or operational data access.
The average hourly rate of freelancers in Frankfurt, Germany who have used Apache Hadoop in their recent projects is 91 €, which corresponds to a daily rate of about 732 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used Apache Hadoop in their recent projects, 100% hold at least a Bachelor's degree, 70% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Apache Hadoop in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Frankfurt, Germany who have used Apache Hadoop in their recent projects are German (100%), English (100%), and French (20%).
The most common industries among freelancers in Frankfurt, Germany who have used Apache Hadoop in their recent projects are Information Technology (90%), Banking and Finance (70%), and Healthcare (60%).
The most common business areas among freelancers in Frankfurt, Germany who have used Apache Hadoop in their recent projects are Information Technology (100%), Business Intelligence (90%), and Product Development (70%).
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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