Databricks Experts in Berlin
matched in minutes by AIHire experts who design lakehouse platforms, build Apache Spark pipelines and operationalize machine learning with MLflow. FRATCH connects you quickly with vetted, available freelancers whose skills fit your Databricks project.
Meet FRATCH Experts in Berlin, who have recently used Databricks
Dmitry P.
Last position:
Freelance Digital Marketing Analyst at Freelance
- Marketing Strategy: Lead the end-to-end analysis and evaluation of cross-channel marketing campaigns across the entire Customer Journey. My focus is identifying optimization potential and deriving clear, actionable recommendations that drive measurable business impact.
- Data Science & AI: Advanced predictive modeling (Churn, LTV), market basket analysis, clustering, and real-time AI-powered audience discovery utilizing RAG/LLMs.
- Marketing Analytics & Measurement: End-to-end attribution analysis, Marketing Mix Modeling (MMM), audience segmentation, conversion path analysis, and A/B testing across all major platforms.
- Data Engineering & Reporting: Designing and managing robust, multi-platform data pipelines (BigQuery, GCP) for data consolidation, automated dashboard generation, and critical API integrations.
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.
Sejal V.
Last position:
Data & ML Engineering at Consulting
- Fractional leadership; consulting growth-stage startups and scale-ups on data strategy, ML products, and platform foundations
- Building decisioning systems for growth, personalization, & product experimentation, across e-Commerce, Digital Health, Energy, and Logistics
- Exploring Agentic AI & LLM-based tooling for production readiness patterns
Tobias L.
Last position:
Data Engineer at unitb consulting GmbH
Tasks: Design and operation of end-to-end cloud data platforms for enterprise clients in publishing and finance, including infrastructure automation, pipeline development, monitoring, and data quality.
Activities:
- Built multi-layer data architectures on Databricks (Apache Spark, Delta Lake), BigQuery, and GCP
- Fully automated cloud infrastructure with Terraform across 3 environments (DEV/STG/PRD)
- Developed automated data pipelines with Python, dbt, and GCP services for different data sources
- Built monitoring and alerting systems for real-time platform monitoring
- Implemented data versioning and quality checks at every layer
- Designed automated test and deployment pipelines in GitLab and Bitbucket
Achievements:
- 2× production data processing capacity, reduced spike response time from minutes to ≤15 s, server errors ≈ 0
- Replaced 3,000 lines of manual configuration with a reusable automation module for 7 customer domains, configuration errors to 0
- Delivered a complete end-to-end data platform at ~€10/month infrastructure cost
- Migrated 7 database tables with 0 downstream issues
- Removed 100% exposed credentials, eliminated external vendor dependency
- Delivered integration of 3 teams in 1 sprint
Diogo S.
Last position:
Backend Engineer and AI Orchestrator at Stealth Startup
- Providing freelance software engineering and AI orchestration services for an early-stage startup.
- Designing and coordinating autonomous AI systems capable of executing complex, multi- step workflows.
- Developing customer-facing pilots and proof-of-concept solutions.
- Participating in meetings with customers and investors to support product development and business discussions.
Hamza K.
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.
Jan K.
Last position:
Data Expert at Manufacturing
Enrico G.
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
Raphael M.
Last position:
Founder / Quant Developer at Market Maker
- Crypto quant strategy development, automated trade execution, onchain data client (Ethereum / Solana)
- Data and trade architecture development for liquidity provision
Santina W.
Last position:
Business Analyst & BI Strategist - Comparison Portal at dataweys (self-employed)
- Assessment of the existing reporting landscape and strategic bundling of needs
- Migration and consolidation of reports to Metabase, connected to ClickHouse as the data foundation
- Building and maintaining data pipelines
Stack: Metabase · ClickHouse · Appsmith · Airflow
Vili D.
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
- Optimized the data architecture (medallion) to better decouple processing stages and improve transparency and reproducibility
- Ensured technical quality of data processing in Databricks by introducing schema enforcement, data quality checks and a structured data architecture
- Orchestrated pipelines with Azure Data Factory
- Professionalized and automated the development and deployment process by integrating Git and GitHub Actions
- Led the Data Engineering team (3 members) in a functional role
- Conducted workshops to optimize and stabilize the data platform and the development process
- Collected and prioritized new requests, maintained the product backlog
- Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS SharePoint, Mural
Bertrand R.
Last position:
Interim IAM Product Owner (Identity Management) at REWE digital GmbH
- Establishing Identity & Directory Management as a new (split-off) team & product within the IAM cluster.
- Leading the “Identity & Directory Management” product (8 people) as Product Owner.
- Concept for ‘Digital Identities’, i.e. IDs with n users/accounts and strategy for a modernized product offering.
- Upgrading APIs, migrating to a containerized infrastructure, rolling out international markets & standardized solutions across the REWE enterprise group.
- Tech: OpenText™ (NetIQ) eDirectory & Identity Manager, LDAP, SAP HR/HCM, Docker/Kubernetes/Podman, REST APIs, Microsoft Active Directory & Entra ID, postgresDB, Keycloak, Ansible, Cyberark (PAM), Apache Kafka, Jira, Confluence, Miro.
Ashwin P.
Last position:
Data Scientist at Mercor Intelligence
- Elevated LLM output reliability by engineering domain-specific prompts and evaluation logic, improving reasoning consistency across production language model workflows.
- Designed advanced coding benchmarks and validated solutions to strengthen training and evaluation datasets, improving model performance on technical problem-solving tasks.
- Designed and implemented automated evaluation frameworks for technical reasoning tasks; optimized LLM output reliability by 15% through rigorous prompt engineering and rubric-based benchmarking.
Joachim G.
Last position:
Software Coordinator / Business Analyst / Developer at Kassenärztliche Vereinigung Sachsen
- Leading coordination between business units and IT
- Coordinating development and testing
- Business analysis and structured requirements gathering
- Specifying functional and technical requirements
- Integrating interfaces to internal systems
- Developing SQL queries and reports
- Documentation in Confluence Result: On-time go-live, structured and agreed project basis, ensuring a coordinated project workflow.
Rohini A.
Last position:
Senior Product Manager at Zalando
- Product strategy & vision: Led campaign performance reporting platform serving 700+ partners, transformed manual MSTR-based weekly reporting to real-time self-service platform enabling partner autonomy and operational efficiency
- Strategic roadmap management: Led phased migration prioritizing Performance campaigns (70% revenue) ahead of Awareness and Engagement, driving iterative platform evolution aligned with objectives, partner feedback, GDPR compliance, and data retention policies
- User research & customer discovery: Conducted regular user interviews with partners to understand reporting needs, decision-making processes, and additional KPI requirements, translating insights into platform enhancements and feature prioritization
- Cross-functional leadership: Collaborated with Product Consultants, analysts, data engineers, frontend teams, and product marketing to execute seamless platform migration, reducing PC team size by 2 FTEs while improving service quality
- Scaled user adoption: Strategically onboarded partners starting with top 30 partner-program partners, expanding to all 700+ partner-program and wholesale partners through user education documentation, training coordination, and iterative feedback incorporation
- Data-driven product optimization: Implemented Google Analytics tracking and engagement monitoring, identified low-engagement features (report downloads, detailed links), deployed AppCues and re-education campaigns resulting in 40% weekly engagement rate
- KPI standardization & governance: Led cross-functional initiative to standardize KPI definitions and formulas across reports, dashboards, and ZMS platform, defined North Star metrics and essential KPIs for each campaign objective ensuring consistent measurement and decision-making
Discover over 15,000 top freelancers
Statistics of experts using Databricks
Aggregated from the professional profiles of matched freelancers.
Experience
12 years (Germany: 15 years)
Position duration
1.9 years (Germany: 2.9 years)
Positions per freelancer
9 (Germany: 10)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
65% (Germany: 68%)
Doctorate
24% (Germany: 17%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
German, English, Spanish
Speak two or more languages
96% (Germany: 97%)
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 Berlin 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 Berlin using Databricks
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.
Databricks 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 (78%)
- Banking and Finance (39%)
- Professional Services (39%)
- Education (35%)
- Automotive (30%)
- Healthcare (30%)
- Energy (26%)
- Manufacturing (26%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Lakehouse foundation
Databricks is a cloud-based data and AI platform built around the lakehouse architecture. It combines scalable data storage with analytics, data engineering, business intelligence and machine learning in one environment. Teams use it to turn raw data into governed tables, models, dashboards and production services.
Core workloads
Databricks supports batch and streaming workloads across customer, operational and machine data. Typical delivery work includes:
- Designing Delta Lake tables and medallion architectures
- Building Apache Spark ETL and streaming pipelines
- Creating SQL warehouses, dashboards and data products
- Training, registering and serving machine learning models
- Migrating legacy warehouses or Hadoop workloads
Ecosystem and tooling
Strong Databricks specialists work across Apache Spark, Delta Lake, Unity Catalog and Databricks SQL. They may also use MLflow for experiment tracking and model lifecycle management, Workflows for orchestration, and Delta Live Tables for managed pipeline development. Cloud knowledge matters because Databricks runs across Microsoft Azure, Amazon Web Services and Google Cloud.
When expertise helps
Companies often bring in freelance expertise when a lakehouse must be designed, a data estate needs migration or pipelines are becoming difficult to operate. Specialist support is also useful when governance, cost control, streaming reliability or machine learning delivery has become a business priority. Berlin teams may combine on-site workshops with remote implementation across international data and product groups.
Delivery skills
A capable professional can translate business questions into robust data models and production-ready workflows. They understand partitioning, schema evolution, incremental processing, performance tuning and access controls, rather than treating notebooks as the finished product. Experience with Python, SQL, Scala, cloud identity and infrastructure automation can strengthen delivery.
Choosing well
Look for evidence of complete Databricks delivery: architecture decisions, tested pipelines, monitored jobs and documented governance. Ask how the specialist approaches data quality, lineage, failure recovery and workload isolation. Strong professionals explain trade-offs between Delta Lake, warehouse patterns and other cloud data services, then leave behind maintainable code, clear runbooks and a platform the internal team can operate.
Frequently asked questions
Questions about Databricks? Start with the answers below.
Databricks is used to build lakehouse platforms for data engineering, analytics and machine learning. Companies use it for batch and streaming pipelines, governed data products, SQL reporting, model development and real-time data processing.
Databricks combines data lake storage, Apache Spark processing, SQL analytics and machine learning in one platform. A traditional warehouse may offer simpler reporting operations, while Databricks is often considered when teams need flexible processing, large-scale data transformation or mixed data and AI workloads.
A strong Databricks specialist commonly brings Apache Spark, Python, SQL, Delta Lake and cloud data platform knowledge. Useful adjacent skills include Unity Catalog, MLflow, infrastructure automation, orchestration, streaming systems and data quality practices.
The right level depends on the scope, not a fixed duration. A focused pipeline may need a specialist who can work within an existing platform, while a new lakehouse requires proven architecture, migration, security, governance and operating model experience.
Yes. Databricks work is well suited to remote collaboration because development, cloud environments and documentation can be shared online. On-site workshops in Berlin can still help with architecture decisions, stakeholder alignment and handover, especially when several teams own the data estate.
Ask for concrete examples of Databricks solutions that reached production, not only notebook prototypes. Explore how the specialist handled testing, monitoring, schema changes, access controls, performance and recovery, and review whether the resulting code and documentation are maintainable.
Databricks supports the full machine learning workflow, including feature preparation, experiment tracking, model registration and serving. MLflow is commonly used with the platform, while specialists may add governance, deployment automation and monitoring to make models reliable in production.
Working with Databricks often involves notebooks, repositories, Workflows, cluster or compute configuration and environment promotion. Freelancers should be comfortable with version control, automated testing, secrets management, cloud permissions and repeatable deployment practices rather than relying on manual workspace changes.
The average hourly rate of freelancers in Berlin, Germany who have used Databricks in their recent projects is 83 €, which corresponds to a daily rate of about 663 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Databricks in their recent projects, 100% hold at least a Bachelor's degree, 65% hold at least a Master's degree, and 24% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Databricks in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Berlin, Germany who have used Databricks in their recent projects are German (100%), English (96%), and Spanish (13%).
The most common industries among freelancers in Berlin, Germany who have used Databricks in their recent projects are Information Technology (78%), Banking and Finance (39%), and Professional Services (39%).
The most common business areas among freelancers in Berlin, Germany who have used Databricks in their recent projects are Information Technology (96%), Product Development (83%), and Business Intelligence (78%).
Main locations of FRATCH Experts, who have recently used Databricks
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.
Hamburg 
Munich 
Cologne 
Frankfurt 
Dusseldorf 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
