Databricks Experts in Berlin
in minutes from over 15,000 CVs with the power of AI.Hire experts who design Lakehouse pipelines, tune Spark workloads, and set up Delta Lake and Unity Catalog for reliable data access. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Berlin, who have recently used Databricks
Dmitry Pankov
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 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.
Sejal Vaidya
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 Lewen
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
Joachim Groth
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.
Diogo Soares
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 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.
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
Raphael Mankopf
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 Wey
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 Dhamo
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 Rothen
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 Parthasarathy
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.
Rohini Adavappa
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: 14 years)
Position duration
1.8 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: 70%)
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 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Lakehouse work
Databricks is used to build data lakehouse systems that bring analytics, engineering, and machine learning together. Specialists work across Databricks notebooks, jobs, Delta Lake, and SQL Warehouses to turn raw data into usable pipelines and governed tables.
Typical deliverables
- Batch and streaming data pipelines
- Delta Lake table design and optimization
- Data quality checks and orchestration
- Spark tuning for large workloads
- Notebook-based analysis and reusable jobs
Core stack
Strong professionals know Apache Spark, SQL, Python, and the Databricks Lakehouse Platform. They also work with Unity Catalog, MLflow, dbt, cloud storage, and warehouse integrations. The best experts keep data access clean and jobs easy to maintain.
When to bring in help
Companies usually need freelance support when a pipeline is slow, a migration is blocked, or a team needs a clean setup for shared data work. Databricks experts also help with platform rollout, cost control, and secure access design. In Berlin, they often support product teams, media companies, and data-heavy services that need flexible delivery.
What good looks like
A strong expert writes clear Spark code, understands distributed processing, and can explain trade-offs in a simple way. They should know how to debug failing jobs, handle schema changes, and keep Delta tables stable. Look for practical experience with production data, not just notebooks.
Working model
Freelance Databricks specialists can join remotely or on-site, depending on the access needs and team setup. For Berlin-based projects, hybrid work is common when workshops, handover sessions, or data governance reviews matter. The best collaboration stays close to the data platform and the business goal.
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. Teams use it to ingest data, transform it with Spark, store it in Delta Lake, and make it available for reporting or downstream apps. It is a fit when one stack must support both pipelines and analysis.
Databricks adds managed tooling around Spark, not just the runtime itself. That means easier job orchestration, shared notebooks, governance features, and built-in support for the lakehouse model. Plain Spark can still be the right choice for smaller or more controlled setups, but Databricks is often chosen when teams want less platform work.
A strong Databricks specialist usually works well with SQL, Python, and cloud storage. Experience with Delta Lake, Unity Catalog, dbt, MLflow, and data modeling is often important too. If your project includes streaming or BI delivery, those skills matter as much as the core platform.
The right level depends on the scope, but Databricks work is rarely just basic notebook use. Simple analysis tasks can be handled by a generalist, while migrations, governance, or production pipelines need someone who has shipped similar work before. For critical data paths, proven production experience matters most.
Yes, most Databricks work can be done remotely if access, security, and communication are set up well. Berlin teams often use remote specialists for pipeline build-out, tuning, and migration work, then bring them in on-site for workshops or handover sessions. Hybrid works well when several teams share the same data platform.
People often say Databricks when they mean the full Databricks Lakehouse Platform. The platform includes Spark-based compute, Delta Lake, governance, notebooks, jobs, and supporting tools around the lakehouse pattern. When you hire, make sure the specialist understands the whole stack, not only one feature.
Look for clear explanations, structured pipelines, and practical debugging habits in Databricks projects. Good specialists can describe how they manage schema changes, performance, access control, and handover. Ask for examples of production systems, not only notebooks or training material.
Berlin companies in media, e-commerce, fintech, SaaS, and mobility often use Databricks for data-heavy work. The platform fits teams that need shared data preparation, analytics, and machine learning in one place. It is especially useful where different teams depend on the same governed data tables.
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 664 € 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 1.8 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
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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Philipp Thomaschewski
FRATCH CEO
