
Data Lake Experts in Munich
matched in minutes by AIHire experts who design scalable data storage, build reliable ingestion pipelines and connect analytics or machine learning workloads across cloud and on-premise environments. FRATCH matches you quickly with vetted, available freelancers whose experience fits your project.
Meet FRATCH Experts in Munich, who have recently used Data Lake
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
Alyosh A.
Last position:
Business Intelligence Consultant at Large Private Equity Group
- Business intelligence and KPI specification and playbook for 35 European companies.
Thomas H.
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).
Hardeep B.
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.
Natalia D.
Last position:
Senior HR BP for Tech and Marketing at KONUX GmbH
- Organizational design & HR transformation project.
- OKR development.
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
Harald L.
Last position:
Project Management at Loocid LLC
- Conducted a comprehensive due diligence review for a potential acquisition of a Swiss manufacturing company as part of a pre-merger analysis
- Assessed production capacities and technical infrastructure
- Evaluated integration possibilities into existing business processes
- Performed risk assessment and developed recommendations for action
- Documented the findings and presented them to management
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)
Bhanu A.
Last position:
Freelance Salesforce CRM and Marketing Cloud Expert at Arkplus
- Engage with stakeholders across departments to identify and document business needs, goals, and objectives.
- Conduct interviews, workshops, and surveys to gather comprehensive requirements for Salesforce solutions.
- Configure Salesforce products such as Sales Cloud, Service Cloud, Marketing Cloud, Health Cloud, and Data Cloud to align with the company's requirements.
- Analyze existing business processes and identify opportunities for improvement and automation within the Salesforce platform.
- Build campaigns, automation and journeys, data management, API and integrations, scripting and customization, analytics and reporting, introduction of generative AI capabilities.
- Develop custom applications using Apex, Visualforce, and Lightning Components; create and customize Salesforce objects, workflows, validation rules, and triggers; implement custom user interfaces using Lightning Web Components (LWC).
- Develop training materials and conduct training sessions to ensure effective user adoption of Salesforce solutions.
- Provide ongoing support to end-users, addressing issues and ensuring they are proficient in using Salesforce tools.
- Create and execute test plans, including unit testing, integration testing, and user acceptance testing (UAT).
- Identify and resolve issues, ensuring that Salesforce solutions meet business requirements and quality standards.
- Design and implement customized reports and dashboards to provide actionable insights and support data-driven decision-making and product delivery.
Nikolay T.
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.
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
Velika I.
Last position:
Senior Program Manager - Data Enablement, Client Experience Office at Sun Life Financial
- Managed a $30M digital portfolio, integrating strategic goals with technological solutions to personalize and enhance customer experience across business units, increasing engagement and satisfaction.
- Collaborated with client experience office leadership and cross-functional teams to define business value, driving digital transformation initiatives and digital practices organization-wide.
- Championed the "Unified View of the Client (UC360)" initiative by leading four agile squads to integrate disparate client data across business units into a single analytics platform, expanding client insights, marketing engagement, and journey analytics.
- Managed an $8M annual budget, promoting adherence to a +/- 5% budget variance, and conducted quarterly incremental planning to synchronize cross-team dependencies.
- Drove a $26M increase in sales and retained assets by utilizing UC360 platform to implement targeted marketing strategies and client outreach, facilitated by predictive models and deep insights, boosting analytics team productivity and speeding up market delivery.
- Resolved misalignments and data inconsistencies by coordinating a third-party assessment to define a new data and analytics target state and operating model, including a governance framework, enabling timely project completion and cross-functional collaboration.
- Administered detailed reporting on key performance indicators (KPIs), financial forecasting, and resource planning for five agile teams, ensuring projects met objectives and generated quantifiable returns on investment, maximizing efficiency and impact across key business areas.
Daniel C.
Last position:
Founder & Managing Director at BotCraft GmbH
- Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
- Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
- Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
- Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
- Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)
Discover over 15,000 top freelancers
Statistics of experts using Data Lake
Aggregated from the professional profiles of matched freelancers.
Experience
19 years (Germany: 18 years)

Position duration
3.3 years (Germany: 2.4 years)

Positions per freelancer
9 (Germany: 10)

Top business areas
Information Technology, Business Intelligence, Project Management

Top industries
Information Technology, Professional Services, Banking and Finance

Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
90% (Germany: 97%)
Master's degree or higher
65% (Germany: 70%)
Doctorate
10% (Germany: 18%)

Certifications per freelancer
3 (Germany: 4)

Most common languages
English, German, Spanish

Speak two or more languages
95% (Germany: 96%)
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 Data Lake
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.
Data Lake 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 (81%)
- Professional Services (62%)
- Banking and Finance (57%)
- Telecommunication (43%)
- Automotive (38%)
- Insurance (38%)
- Manufacturing (38%)
- Transportation (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What a Data Lake does
A Data Lake stores raw, structured, semi-structured and unstructured data in its original form. It gives teams one place to collect events, files, records and machine data before they decide how each source should be modeled. Experts establish the storage, cataloging and access patterns that keep this foundation usable rather than turning it into an unmanaged data dump.
Where companies use it
Data Lakes support broad analytical and operational workloads across industries, including manufacturing, mobility, retail, finance and life sciences. Common deliverables include:
- Centralized ingestion from applications, databases, APIs and devices
- Curated zones for reporting, exploration and machine learning
- Historical data stores for forecasting, monitoring and audit needs
- Lakehouse designs that combine flexible storage with warehouse-style queries
Ecosystem and tooling
The work often spans object storage such as Amazon S3, Azure Data Lake Storage or Google Cloud Storage, together with Apache Spark, Apache Kafka, Trino or Databricks. Specialists also work with Delta Lake, Apache Iceberg or Apache Hudi for table management, schema evolution and reliable transactions. Catalogs, orchestration tools and infrastructure-as-code complete the operating model.
When to bring in expertise
Companies usually seek freelance expertise when data sources are multiplying, existing reporting cannot scale, or a cloud migration needs a clear target architecture. A specialist can define ingestion contracts, partitioning, retention and governance before implementation begins. In Munich, remote collaboration is common, while regulated or plant-based work may require focused on-site sessions.
Skills that make the difference
Strong professionals understand both data engineering and the business questions behind the platform. They can profile source data, choose batch or streaming patterns, manage schemas and optimize query performance without losing traceability. They also bring practical knowledge of IAM, encryption, lineage, data quality checks, cost control and CI/CD for data workloads.
How quality is assessed
Look for delivered Data Lake solutions with clear ownership of ingestion, storage, transformation and consumption layers. Ask how the specialist handled late data, changing schemas, failed pipelines and access boundaries. Good documentation, automated tests, observable workflows and a measured migration plan show that the design can operate beyond a proof of concept.
Frequently asked questions
Key details about Data Lake, drawn from the questions we get asked most.
A Data Lake is used to collect and retain large volumes of raw data from many sources. Teams can later transform that data for business intelligence, advanced analytics, machine learning, monitoring or operational use.
A Data Lake usually stores data in varied formats with more flexible modeling, while a warehouse focuses on structured, prepared data for consistent reporting. Many modern architectures use both, or adopt a lakehouse approach to combine flexible storage with warehouse-style governance and queries.
A strong Data Lake specialist should understand cloud object storage, SQL, distributed processing and pipeline orchestration. Experience with Apache Spark, Apache Kafka, Databricks, Delta Lake, Apache Iceberg, security and data catalogs is also valuable.
The required depth depends on the project’s data volume, sources, compliance needs and operating model. A migration or greenfield platform benefits from a specialist who has handled architecture, governance and production operations, while a focused ingestion task may need a narrower skill set.
Yes. Data Lake work is often suitable for remote collaboration because design, configuration, documentation and code reviews happen online. On-site workshops can still help when teams need to map factory systems, regulated processes or complex internal data ownership.
Ask for examples of production data platforms, not only prototypes. Discuss source-system integration, schema changes, quality controls, security, observability, cost management and how the specialist explains trade-offs to analysts and business stakeholders.
A Data Lake can fit better when a company needs to retain diverse raw data, support exploratory work or serve machine learning alongside reporting. A warehouse may be preferable when the main requirement is tightly governed, predictable analytics on well-structured data.
A Data Lake professional can demonstrate quality through clear architecture decisions, reliable pipeline tests, lineage and access documentation. They should also explain how they handled failures, duplicate records, late-arriving data, changing schemas and performance or storage costs.
The average hourly rate of freelancers in Munich, Germany who have used Data Lake in their recent projects is 103 €, which corresponds to a daily rate of about 820 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Data Lake in their recent projects, 90% hold at least a Bachelor's degree, 65% hold at least a Master's degree, and 10% hold a doctorate.
On average, freelancers in Munich, Germany who have used Data Lake in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 3.3 years.
The most common languages among freelancers in Munich, Germany who have used Data Lake in their recent projects are English (100%), German (90%), and Spanish (29%).
The most common industries among freelancers in Munich, Germany who have used Data Lake in their recent projects are Information Technology (81%), Professional Services (62%), and Banking and Finance (57%).
The most common business areas among freelancers in Munich, Germany who have used Data Lake in their recent projects are Information Technology (95%), Business Intelligence (76%), and Project Management (67%).
Main locations of FRATCH Experts, who have recently used Data Lake
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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