Data Lakehouse Experts in Munich
in minutes from over 15,000 CVs with the power of AI.Hire experts who design lakehouse architectures, unify batch and streaming data, and tune Delta Lake or Apache Iceberg for reliable analytics. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used Data Lakehouse
Deepa K.
Last position:
Data Analyst – BI Lead Engineer at Novartis
- Leading enterprise BI transformation across Power BI & Microsoft Fabric, delivering scalable data models, automated reporting, and high-performance analytics solutions for commercial and operational leadership.
- Building and optimizing Power BI Dataflows, Fabric Lakehouse datasets, semantic models, and automated reporting pipelines to improve data scalability, governance, and reporting performance.
- Driving dashboard modernization and KPI governance by translating complex business requirements into executive-level insights, interactive visualizations, and decision-ready analytics.
- Designing end-to-end Microsoft Fabric architectures integrating data ingestion, transformation, virtualization, and enterprise reporting across cross-functional business domains with SAP BW to Qlik to Power BI migration.
- Delivering AI-enabled reporting capabilities, threshold-based alerting, and automation frameworks within the Power BI ecosystem to accelerate business decision-making.
- Partnering with commercial leadership, analytics teams, and IT stakeholders to standardize KPIs, optimize BI strategy, and deliver scalable, business-critical reporting solutions.
- Recognized for combining strong stakeholder leadership, technical architecture expertise, and business-driven analytics to deliver impactful enterprise BI transformation initiatives.
Ajay Kumar D.
Last position:
Senior BI and Analytics Engineer at Novartis
- Led enterprise reporting modernization by migrating legacy SSRS reporting solutions to Power BI, supporting 500+ business users while ensuring full GDPR/DSGVO compliance.
- Designed and optimized Power BI and Microsoft Fabric semantic models using star schema, dimensional modeling, advanced DAX, and performance optimization techniques, reducing query latency by 25%.
- Delivered 20+ executive and operational dashboards featuring KPI scorecards, drill-through, bookmarks, and row-level security, improving reporting efficiency by 20%.
- Enabled self-service analytics through governed Power BI datasets, dataflows, and gateway architecture, increasing business-led reporting adoption by 35%.
- Configured an incremental refresh policy and query folding for a 50+ million row sales dataset, reducing daily report refresh times by 85%.
- Deployed automated ETL/ELT pipelines using Azure Data Factory, Microsoft Fabric, and Snowflake, reducing reporting delivery timelines by 40% through workflow automation.
- Spearheaded Microsoft Fabric analytics modernization initiatives including lakehouse architecture, OneLake integration, and centralized data platform development, reducing data latency from 2 hours to 20 minutes.
- Translated business requirements from 15+ stakeholders into scalable Power BI semantic models and dashboards, improving reporting consistency and reducing ad-hoc reporting requests by 25%.
- Applied Microsoft Copilot and generative AI tools to accelerate SQL development, DAX authoring, technical documentation, and testing activities, reducing development effort by approximately 15 hours per week.
Emanuel F.
Last position:
Interim Architect & Data Taskforce at Freelancer / Project Assignments
- Data Engineering: Design and implementation of scalable data pipelines
- Legacy migrations to Microsoft Fabric (Lakehouse, Dataflows Gen2, Pipelines)
- BO Universe migrations to MS Fabric / Semantic Models / Power BI
- Taskforce for data-driven transformation projects involving Azure Fabric / Oracle / MSSQL
Sara Z.
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%.
Nima N.
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
Alyosh A.
Last position:
Business Intelligence Consultant at Large Private Equity Group
- Business intelligence and KPI specification and playbook for 35 European companies.
Max R.
Last position:
Cloud (AWS) | AI | DevOps | Data at Boehringer Ingelheim
- Architected and implemented an enterprise-grade AI Agent Platform leveraging Retrieval Augmented Generation (RAG) architecture to enhance clinical data insights.
- Established robust CI/CD pipelines for LLM applications using CDK and Jenkins, significantly reducing deployment times.
- Implemented comprehensive observability solutions that increased agent reliability across pharmaceutical environments.
- Designed scalable AI workflows with advanced orchestration that optimized context handling for enterprise data sources.
- Technologies: AI Agents (LangChain, LangGraph, Bedrock, Smolagents, Streamlit); LLM Operations (Tracing, Testing, Evaluation, LangSmith, LangFuse); Infrastructure-As-Code (AWS CDK, Terraform, Typescript, Jenkins); Vectors, Embeddings, RAG (OpenSearch, pgvector, PDF Extraction)
Martin M.
Last position:
Product Owner AI Learning Platform at B2B Tech Scale-Up
- Agile setup of a multimodal analysis platform for training materials (video, audio, documents) using Scrum
- Extraction of context-relevant content based on user profiles & competency dimensions
- Personalized delivery of learning content to boost sales performance
- Close coordination with sales teams & stakeholders to validate features
- Use of Gemini, Whisper, Python & JavaScript, deployment on AWS, Perl for scripting data imports
- Integration into existing tools & CRM systems for smooth adoption
- Technologies used: Python, OpenAI, DB tech like PostgreSQL, CI/CD for Airflow DAGs, FastAPI
Discover over 15,000 top freelancers
Statistics of experts using Data Lakehouse
Aggregated from the professional profiles of matched freelancers.
Experience
18 years (Germany: 14 years)
Position duration
2.1 years
Positions per freelancer
11 (Germany: 10)
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Information Technology, Manufacturing, Automotive
Certification focus areas
Business Intelligence, Information Technology, Marketing
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
75% (Germany: 55%)
Doctorate
25% (Germany: 14%)
Certifications per freelancer
3 (Germany: 4)
Most common languages
German, English, Spanish
Speak two or more languages
100%
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Munich using Data Lakehouse
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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Data Lakehouse 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 (75%)
- Manufacturing (75%)
- Automotive (63%)
- Pharmaceutical (63%)
- Professional Services (63%)
- Banking and Finance (50%)
- Transportation (50%)
- Energy (38%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Lakehouse basics
A data lakehouse combines the flexibility of a data lake with the reliability and performance patterns of a data warehouse. It is used to build one place for raw, curated, and analytics-ready data without moving every workload into a separate system. Many teams also search for the lakehouse pattern, Delta Lake, or Apache Iceberg when they mean this setup.
Typical use cases
- Central analytics data layer for BI and reporting
- Streaming and batch pipelines in one architecture
- Machine learning feature and training data prep
- Governed self-service data access for many teams
The model fits companies that need fast access to large, mixed data sets and still want strong control over quality and schema. In Munich, it often matters in automotive, industrial, insurance, and software teams that work across cloud and hybrid environments.
Ecosystem and tools
A strong lakehouse stack often includes Spark, SQL engines, table formats such as Delta Lake or Apache Iceberg, orchestration, catalog, and governance tools. Professionals also work with object storage, notebook workflows, and cloud services from AWS, Azure, or Google Cloud. The exact mix depends on latency, scale, and how many users need to query the data.
When freelance help matters
Companies bring in freelance specialists when a lakehouse design must be chosen, rebuilt, or stabilized quickly. Typical signs are fragmented data pipelines, slow queries, unclear data ownership, or repeated problems between analytics and data engineering teams. Freelancers can also support migrations from older warehouse or Hadoop setups.
What strong specialists do
- Design clean storage, partitioning, and table layouts
- Set up ingestion, transformation, and testing flows
- Define governance, access rules, and data contracts
- Improve query performance and pipeline reliability
Strong professionals understand both data engineering and analytics needs. They know how to balance openness for exploration with guardrails for production use. They also document decisions clearly so internal teams can maintain the platform after the engagement.
Working in Munich
Munich teams often want specialists who can collaborate with analysts, platform teams, and business stakeholders in English, and sometimes in German as well. On-site workshops help during architecture reviews and migration planning, while much of the implementation work can be done remotely. The best results come from experts who can explain trade-offs simply and adapt to the local operating model.
Frequently asked questions
Need clarity? These are the questions we hear most often about Data Lakehouse.
A strong Data Lakehouse setup is used to combine raw storage, curated datasets, and analytics in one architecture. It supports BI, machine learning, and operational reporting without forcing every team onto separate systems. That is why many companies choose it when they need both flexibility and control.
A lakehouse sits between the two. Compared with a data lake, it adds more structure, governance, and query reliability; compared with a warehouse, it handles more open data formats and large-scale pipeline workflows. Teams usually pick it when they want one shared layer instead of multiple disconnected systems.
Data Lakehouse projects often include Apache Spark, SQL query engines, Delta Lake, Apache Iceberg, and catalog or governance tools. Cloud storage and orchestration are also common parts of the stack. A good specialist should understand how these pieces fit together, not just how to use one tool.
You usually need a Data Lakehouse specialist when a new architecture must be designed, an old setup must be migrated, or pipelines have become hard to trust. It also helps when query performance, data quality, or access control is causing friction across teams. Freelance support is useful when internal staff are already tied up on delivery work.
A lakehouse project needs more than basic SQL or notebook skills. The specialist should understand data modeling, storage layout, streaming or batch ingestion, governance, and how to debug performance issues. For larger initiatives, it is important that the person has shipped production data systems, not just prototypes.
Yes, most Data Lakehouse work can be done remotely because the main tasks are architecture, implementation, and review. In Munich, on-site time is most helpful for kickoff workshops, stakeholder alignment, and migration planning. A hybrid setup often works best when many teams depend on the platform.
A strong Data Lakehouse expert can explain why a design choice was made and what trade-offs it creates. Look for clear thinking on governance, performance, testing, and long-term maintenance, not just tool names. Good work also leaves behind documentation that your team can actually use.
A lakehouse engagement often touches analytics, platform, and business teams at the same time. Freelancers should expect changing requirements, data ownership questions, and a need to document decisions carefully. The best projects are the ones where access, goals, and success criteria are clear from the start.
The average hourly rate of freelancers in Munich, Germany who have used Data Lakehouse in their recent projects is 105 €, which corresponds to a daily rate of about 837 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Data Lakehouse in their recent projects, 100% hold at least a Bachelor's degree, 75% hold at least a Master's degree, and 25% hold a doctorate.
On average, freelancers in Munich, Germany who have used Data Lakehouse in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Munich, Germany who have used Data Lakehouse in their recent projects are German (100%), English (100%), and Spanish (13%).
The most common industries among freelancers in Munich, Germany who have used Data Lakehouse in their recent projects are Information Technology (75%), Manufacturing (75%), and Automotive (63%).
The most common business areas among freelancers in Munich, Germany who have used Data Lakehouse in their recent projects are Business Intelligence (100%), Information Technology (100%), and Product Development (63%).
Main locations of FRATCH Experts, who have recently used Data Lakehouse
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.
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Berlin Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
