Data Lakehouse Experts in Berlin
, matched in minutes from over 15,000 CVs with the power of AIHire experts who design unified data platforms, build batch and streaming pipelines, and govern analytics workloads across technologies such as Databricks, Apache Spark and Delta Lake. FRATCH connects you quickly with vetted, available freelancers who match your technical needs precisely.
Meet FRATCH Experts in Berlin, who have recently used Data Lakehouse
Alexander Z.
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.
Hamza K.
Last position:
Academic Research Contributor in Health Sector (Volunteer) at n/a
- 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.
Mahboobeh S.
Last position:
Senior Analytics Engineer at Jadoosoft
Engaged as an independent BI consultant for a FinTech SaaS platform, building and maintaining the end-to-end Microsoft Azure and Fabric analytics stack behind a multi-tenant accounting product used by business owners to track their own financial performance.
- Partnered with finance stakeholders to translate complex accounting workflows into real-time BI dashboards (P&L, Cash Flow, Balance Sheet), helping 2000+ business owners track cash flow and overall financial health effortlessly.
- Developed 10+ interactive dashboards, giving each business owner personalized, real-time visibility into their own sales performance, accounts receivable aging, and operational costs.
- Enforced strict enterprise data governance by implementing dynamic Row-Level Security (RLS) via DAX to isolate each business owner's data within the shared multi-tenant model.
- Maintained and extended an Azure ELT pipeline (Azure Data Factory, Azure SQL Database, Azure Synapse) that automated data ingestion and transformation.
- Contributed to a team-wide platform migration from Azure to Microsoft Fabric to optimize capacity licensing costs.
- Designed and implemented a Microsoft Fabric ELT pipeline (Lakehouse, Pipelines, PySpark) that automated data ingestion and transformation, replacing the prior Azure pipeline.
- Modeled cleansed data into a Star Schema within the data warehouse using T-SQL, powering the Power BI Semantic Model.
- Leveraged AI tools, including Copilot and Claude, as development partners across the entire ELT pipeline, from data ingestion to KPI reporting and visualization.
- Supported agile delivery processes, using Jira to track development tasks and Confluence to maintain technical and project documentation.
Jan K.
Last position:
Data Expert at Manufacturing
Germo G.
Last position:
BI Developer at Rhenus Logistics
- Business intelligence development in the logistics sector.
- Migrating existing reporting and analysis solutions from Cognos to Microsoft BI.
- Using Microsoft SQL Server, SSAS, Power BI, and other Microsoft BI platform components.
Christian R.
Last position:
Freelance Data Engineer at Ingenieurbüro Christian Richter – Data, Cloud & Container
- Contributed to over 20 successful projects
Enrico G.
Last position:
Freelance Software & Data/AI Engineer at Freelance Software & Data/AI Engineer
- Lecturer for the GenAI Track at Master School Institute of Technology
- Development of a full-stack AI application (React + Python/FastAPI) for automated supplier product imports with intelligent column and category classification (4-layer hierarchy), including human-in-the-loop validation
Discover over 15,000 top freelancers
Statistics of experts using Data Lakehouse
Aggregated from the professional profiles of matched freelancers.
Experience
17 years (Germany: 15 years)
Position duration
2.5 years (Germany: 2.1 years)
Positions per freelancer
12 (Germany: 10)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Healthcare
Certification focus areas
Information Technology, Business Intelligence, Operations
Bachelor's degree or higher
80% (Germany: 97%)
Master's degree or higher
40% (Germany: 60%)
Certifications per freelancer
2 (Germany: 4)
Most common languages
German, English, Persian
Speak two or more languages
100%
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 Berlin 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 Berlin 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 29 Oct 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 (86%)
- Banking and Finance (43%)
- Healthcare (43%)
- Professional Services (43%)
- Automotive (29%)
- Energy (29%)
- Transportation (29%)
- Manufacturing (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
A unified data foundation
A data lakehouse combines the low-cost storage and flexibility of a data lake with the governance and performance expected from a data warehouse. It stores structured, semi-structured and unstructured data in open formats while supporting reliable SQL analytics, machine learning and reporting from shared data.
What it enables
Lakehouses support a broad range of data products and analytical workloads:
- Curated data marts for finance, sales and operations
- Streaming pipelines for events, IoT and application data
- Feature stores and training datasets for machine learning
- Self-service analytics on trusted, reusable data
Ecosystem and tooling
Professionals work across cloud storage, catalogues, processing engines and table formats. Common components include Databricks, Apache Spark, Delta Lake, Apache Iceberg, Apache Hudi, Trino, dbt, Airflow and cloud services from AWS, Microsoft Azure and Google Cloud. Strong delivery also depends on SQL, Python, orchestration and infrastructure automation.
When expertise matters
Companies bring in freelance specialists when a lakehouse must replace fragmented warehouses and lakes, support a new analytics product or improve unreliable pipelines. It is also valuable during platform selection, migration, cost control, data quality work and the introduction of governance across multiple teams.
Signs you need support
The need is usually clear when teams cannot agree on trusted datasets or spend too much time moving data between systems:
- Reports depend on duplicated extracts and manual fixes
- Streaming and batch data follow different definitions
- Access rules, lineage or retention are difficult to enforce
- Query performance and cloud storage costs are unpredictable
What strong professionals deliver
The best specialists connect architecture with practical delivery. They define table layouts, ingestion patterns, medallion or domain structures, quality checks and access policies, then document how the platform will operate. They explain trade-offs between open formats and managed services, test recovery paths and leave teams with maintainable pipelines rather than an isolated prototype.
Frequently asked questions
Curious about Data Lakehouse? Here are the answers that come up again and again.
A Data Lakehouse is used to store and process many types of data in one governed environment. Companies use it for business intelligence, operational reporting, machine learning, streaming analytics and reusable data products.
A Data Lakehouse keeps the flexible, open storage model associated with a data lake while adding warehouse-style governance, performance and SQL access. It can reduce duplication, but a traditional warehouse may remain a better fit for a narrow reporting workload with established structures.
A strong Data Lakehouse specialist usually works with SQL, Python, Apache Spark, cloud storage, orchestration and infrastructure automation. Experience with Databricks, Delta Lake, Apache Iceberg, dbt, catalogues, data quality and access controls is also valuable.
A Data Lakehouse project needs enough experience to cover architecture, ingestion, modelling, governance and operations, not just notebook development. The right specialist should have delivered comparable pipelines or analytical platforms and be able to explain design decisions in the context of your data and teams.
A Data Lakehouse engagement can usually be handled remotely because the work is performed in cloud environments, repositories and collaboration tools. On-site sessions in Berlin can still help with discovery, data ownership decisions and workshops, while German or English language expectations should be agreed early.
A reliable Data Lakehouse has clear ownership, documented data contracts, traceable lineage, automated quality checks and controlled access. Review how the specialist handles schema changes, late-arriving data, failures, recovery, observability and cloud costs before accepting a design.
A Data Lakehouse is attractive when teams need shared data for BI, machine learning and streaming without maintaining repeated copies across separate systems. Separate platforms may still be justified by strict workload isolation, existing investments or specialised performance requirements.
A capable Data Lakehouse freelancer should provide an architecture decision record, source-to-target designs, ingestion and transformation pipelines, table and partitioning standards, governance rules and operational documentation. They should also define tests, monitoring, deployment processes and a clear handover for the internal team.
The average hourly rate of freelancers in Berlin, Germany who have used Data Lakehouse in their recent projects is 101 €, which corresponds to a daily rate of about 806 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Data Lakehouse in their recent projects, 80% hold at least a Bachelor's degree and 40% hold at least a Master's degree.
On average, freelancers in Berlin, Germany who have used Data Lakehouse in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers in Berlin, Germany who have used Data Lakehouse in their recent projects are German (100%), English (100%), and Persian (14%).
The most common industries among freelancers in Berlin, Germany who have used Data Lakehouse in their recent projects are Information Technology (86%), Banking and Finance (43%), and Healthcare (43%).
The most common business areas among freelancers in Berlin, Germany who have used Data Lakehouse in their recent projects are Information Technology (100%), Business Intelligence (86%), and Product Development (71%).
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 
Munich Request a free demo
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Philipp Thomaschewski
FRATCH CEO
