Data Lakehouse Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Data Lakehouse
Jens Henneberg
Last position:
Interim CTO (occasional assignments) at Fujitsu / FSAS
Stabilizing an Azure/.NET landscape in live operation.
- Architecture, DevOps, and operational readiness; technical decisions under time pressure
- Azure DevOps, monitoring, ETL/ELT, cloud security, FinOps, and data-mesh-related topics
Technologies: Azure DevOps, .NET, CI/CD, monitoring, FinOps
Fadi Shoaa
Last position:
Development of a production-ready Enterprise Document AI & Recommendation Platform at Freelancer
- Development of a production-ready Enterprise AI solution for the automated processing of invoices and business documents
- Integration of Azure AI Document Intelligence and LLM technologies into existing business processes
- Development of robust REST APIs for automated document processing and system integration
- Extraction, validation, and storage of structured invoice data in Azure SQL as a base for analytics and machine learning models
- Development of an AI-based recommendation engine with machine learning and deep learning to generate personalized product recommendations based on historical purchase data
- Implementation of logging, monitoring, error handling, and validation mechanisms for stable production use
- Collaboration with business teams to define business rules and integrate the solution into existing enterprise processes
Technologies: Python, Azure AI Document Intelligence, Azure OpenAI, Azure SQL Database, REST APIs, Machine Learning, Deep Learning, OCR, Pandas, JSON, Workflow Automation
Ajay Kumar Deekonda
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.
Alexander Zhirov
Last position:
Senior Data Solutions Engineer at VMware Inc.
- Architected and deployed private cloud data platform on VMware vSphere, integrating Greenplum MPP, Apache Kafka, Kubernetes, and Apache Solr, and developed real-time ingestion pipelines with Kafka Connect and Schema Registry.
- Led Oracle Exadata to Greenplum migration, rearchitected data models, optimized storage, implemented RabbitMQ with Debezium for CDC, and deployed VectorDB for Generative AI.
- Designed and executed multi-cloud migration PoC across AWS, Azure, and GCP, defined KPIs for throughput, latency, and cost efficiency, executed bulk data transfers, validated analytics and streaming workloads, and delivered full-scale architecture recommendations.
- Assessed legacy on-premises infrastructure and designed modern cloud-native data platforms using Greenplum and containerized microservices, advising on scalability, disaster recovery, and high-availability.
Maximilian Braun
Last position:
CTO at nikan.ai
Leading the technical vision and product strategy for a sovereign AI startup focused on European data infrastructure and compliance. Managing a cross-functional team of 7 across engineering, AI development, and operations in a fully remote environment.
- Defining the company's product and technology roadmap, including AI-powered solutions with integrated payment services
- Designing scalable platform architectures with emphasis on data sovereignty, security, and European regulatory compliance
- Driving hands-on development across the full stack while establishing engineering best practices and DevOps workflows
- Enabling developer productivity through mentoring, architectural guidance, and tooling decisions
- Shaping the long-term technical strategy to position the company for sustainable growth
Marco Lindner
Last position:
Senior IT Consultant | Cloud Data Engineer | Infrastructure Architect at Hannover Rück SE
Built an enterprise data lakehouse platform on Azure Databricks
Developed production data pipelines and governance structures
Implemented private cloud infrastructures using Terraform
Introduced modern CI/CD standards in Azure DevOps
Implemented secure IAM and governance concepts
Developed scalable PySpark and Delta Lake frameworks
Supported self-service analytics and data product approaches
Provided architecture and platform consulting for enterprise data initiatives
Built a central DataHub architecture for insurance data
Integrated multiple subsystems into a lakehouse platform
Introduced data governance and data lineage
Supported modern analytics and reporting standards
Optimized data delivery for business and analytics teams
Hamza Khan
Last position:
Academic Research Contributor in Health Sector (Volunteer)
- Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
- Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.
Ludo Prokop
Last position:
Senior Consultant at Insurance
- DWH modernization
- Migration from Informatica PowerCenter to IDMC/CDI
- Migration from IBM DB2 to Databricks
Technologies: Informatica IDMC, Databricks
Monika Thepale
Last position:
Senior ETL Lead at Takeda GmbH
- Led design, development, and deployment of data solutions supporting a major pharma acquisition for Takeda Pharmaceutical Company, delivering transparency reporting systems across Azure,Databricks (Python and Shell Scripting) platforms.
- Owned,Designed and developed scalable ELT pipelines to process Customer and Product data using Azure, complex SQL, Databricks, and shell scripting, enabling efficient data integration and processing across multiple sources including job orchestration and workflow automation.
- Implemented performance optimization techniques (query tuning, parallelism, workload optimization), improving system efficiency and processing time.
- Applied strong analytical and problem-solving skills to assess technical solutions and support business requirements for compliance and transparency reporting.
- Designed scalable data foundations suitable for downstream analytics and AI workloads.
- Led data quality initiatives by assessing multiple source data, defining quality metrics, and establishing processes for monitoring and continuous improvement.
Thore Fahrtmann
Last position:
Data & AI Consultant at ContiTech GmbH
- Consultancy Databricks Lakehouse Platform Architecture
- Migration of existing manufacturing data services to Databricks. Existing services are running on various platforms & tools and are unified on target platform
- Implementation of new Data & AI use cases on Databricks platform (e.g. connecting new systems, building AI Agent prototypes, …)
Tech Stack: Databricks, Azure, PySpark, Python
Jan Krol
Last position:
Data Expert at Manufacturing
Enrico Goerlitz
Last position:
Freelance Software & Data/AI Engineer at Freiberuflicher Software & Data/AI Engineer
- Lecturer for the GenAI Track at the Master School Institute of Technology
- Development of a full-stack AI application (React + Python/FastAPI) for automated supplier product import with intelligent column and category classification (4-layer hierarchical) including human-in-the-loop validation
Sara Zarei
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%.
Albert Frischmann
Last position:
Lead Product Owner at CMBlu Energy AG
- Lead Product Owner for 4 development teams
- Leading and coordinating a greenfield project with parallel implementation of core components by independent teams; managing dependencies and resources
- Establishing a data lakehouse approach, including analysis of data volumes and future requirements as part of a cloud migration (best-of-breed approach)
- Responsible for requirements analysis, selection, and piloting of a LIMS/ELN system, supported by advising decision-makers and managing external vendors
- Introducing and managing an OpenWeb UI and Azure OpenAI-based RAG system to support knowledge extraction and data-driven analyses
- Setting up, configuring, and managing Jira projects, as well as developing project-specific workflows and automations
- Implementing classic Scrum processes with all ceremonies and taking on the Scrum Master role for all involved teams
- Assisting in hiring through interviews and assessments from a product owner's perspective
- Making key architectural decisions, including selecting the platform for the data lakehouse (Databricks) and the strategic integration of LIMS and analytics platforms
Nima Nooshi
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
Discover over 15,000 top freelancers
Statistics of experts using Data Lakehouse
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
2.1 years
Positions per freelancer
9
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Professional Services, Automotive
Certification focus areas
Information Technology, Business Intelligence, Operations
Bachelor's degree or higher
96%
Master's degree or higher
57%
Doctorate
13%
Certifications per freelancer
4
Most common languages
German, English, Spanish
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany 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 Germany 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Lakehouse basics
A data lakehouse combines the flexibility of a data lake with the structure and reliability of a warehouse. It is used for BI, reporting, data science, and machine learning on the same data layer. Teams often use the term lakehouse architecture when they want one place for raw, curated, and serving data.
Core stack
- Delta Lake, Apache Iceberg, or Apache Hudi table layers
- Spark, SQL engines, and notebook workflows
- Batch and streaming pipelines with schema control
- Catalog, governance, and data quality rules
Strong specialists know how these parts fit together. They make sure tables stay queryable, pipelines stay recoverable, and analysts do not fight with broken file layouts or inconsistent schemas.
Where it fits
Lakehouse projects show up when companies need fast analytics on large, mixed data sets. Common use cases include customer analytics, product telemetry, fraud signals, and feature stores for ML. In Germany, this often matters for manufacturing, mobility, retail, finance, and other data-heavy teams that run on both cloud and hybrid setups.
When to bring in help
Bring in freelance expertise when a warehouse is too rigid, a data lake is too messy, or both systems are duplicating work. It also helps during migrations from classic warehouse stacks, when teams need better governance, or when Databricks Lakehouse, Snowflake, or open lakehouse tools must be aligned with existing processes.
What good specialists do
- Design table layouts and partitioning that support real query patterns
- Set up ingestion, transformation, and incremental refresh logic
- Improve reliability with schema evolution, validation, and versioning
- Support cost control, performance tuning, and access rules
Good professionals write clear pipelines, document trade-offs, and leave structures that other specialists can maintain. They also understand the difference between storage, compute, and governance, which is critical in a lakehouse.
Collaboration and handover
Lakehouse work is often remote, but on-site sessions help with architecture decisions, stakeholder alignment, and access to internal data teams. For Germany, many companies expect clear English in the technical flow, while documentation or workshops may also need German. A strong freelancer can work with both and keep handover practical.
Frequently asked questions
Before you brief your next project: the most common questions about Data Lakehouse.
A data lakehouse is used to combine flexible storage with warehouse-style querying. Companies use it for BI dashboards, ad hoc analysis, machine learning features, and data sharing across teams. It is a good fit when the same data must serve both analysts and specialists without copying everything into separate systems.
A lakehouse keeps more of the low-cost, open storage model from a data lake while adding table management, consistency, and SQL access. A warehouse is usually more controlled and easier to standardize, but it can be less flexible for raw or semi-structured data. The choice depends on how much variety, scale, and experimentation the company needs.
No. Delta Lake is a table format and transaction layer that is often used to build a lakehouse. The broader lakehouse includes the storage layer, compute engines, catalogs, security, and data workflows around it. In many projects, people use Delta Lake, Apache Iceberg, or Apache Hudi as the foundation.
A strong Data Lakehouse specialist usually knows Spark, SQL, data modeling, orchestration, and table formats such as Iceberg or Hudi. They should also understand data quality, lineage, governance, and performance tuning. If the stack runs on Databricks, experience with that environment is especially useful.
Projects with mixed batch and streaming data, shared analytics and ML needs, or difficult migration paths usually need deep Data Lakehouse experience. The same is true when teams need to replace brittle file-based pipelines or unify scattered data domains. Simple reporting tasks alone usually do not justify that level of expertise.
Yes, most Data Lakehouse work can be done remotely. Architecture reviews, pipeline build-out, and performance tuning are often easier this way because the specialist can work directly in the cloud environment. On-site time in Germany is mainly useful for stakeholder workshops, access planning, and handover sessions.
A good lakehouse specialist can explain why they chose a given table format, partitioning strategy, and orchestration flow. They should show examples of incremental processing, schema evolution, validation, and recovery from failures. Clean documentation and practical handover matter as much as the code itself.
Choose an open Data Lakehouse stack when portability, control, and long-term flexibility matter most. Choose a vendor-led setup when the team needs faster rollout, tighter integration, and less operational overhead. A good freelancer can compare the options and fit the design to your existing data platform.
The average hourly rate of freelancers in Germany who have used Data Lakehouse in their recent projects is 106 €, which corresponds to a daily rate of about 848 € based on an 8-hour working day.
Of the freelancers in Germany who have used Data Lakehouse in their recent projects, 96% hold at least a Bachelor's degree, 57% hold at least a Master's degree, and 13% hold a doctorate.
On average, freelancers in Germany who have used Data Lakehouse in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Germany who have used Data Lakehouse in their recent projects are German (100%), English (97%), and Spanish (10%).
The most common industries among freelancers in Germany who have used Data Lakehouse in their recent projects are Information Technology (86%), Professional Services (59%), and Automotive (45%).
The most common business areas among freelancers in Germany who have used Data Lakehouse in their recent projects are Information Technology (100%), Business Intelligence (90%), and Product Development (59%).
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 
Munich 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
