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Databricks Expert

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Hire experts who design lakehouse architectures, build Delta Lake pipelines and deliver reliable analytics or machine learning workflows. Get fast, precise matching with vetted, available freelancers who fit your Databricks project.

Meet FRATCH Experts who have recently used Databricks

Verified expert

Henry H.

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Interim CISO, DPO, AI Officer

Essen
Henry H.

Last position:

Interim Manager IT-Compliance at Int. Fertigungsunternehmen

  • Industry: mechanical engineering, vehicle manufacturing
  • Regulations: Data Act
  • Project focus: data governance, legally compliant use of machine data, data platforms
  • Assigned by: CFO, platform product owner

Successes/Results (early phase):

  • Compliance support for the setup of an internal standardized data usage platform based on Databricks.
  • Created the basis for the legally compliant and effective use of machine data, including:
  • Technical: gap analysis and closing of gaps in the segmentation and maintenance of collected machine data.
  • Technical: consideration of data flows from the platform to users and third parties.
  • Organizational: drafting and finalizing the required data usage agreements.
Verified expert

Dmitry P.

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Freelance Digital Marketing Analyst

Berlin
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.
Verified expert

Michael N.

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Senior ML Engineer | AI Engineer | Problem Solver

Eichenau
Michael N.

Last position:

Senior AI Engineer | Forward Deployed Engineer at Tiefbau

  • Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
  • Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
  • Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Verified expert

Mirza K.

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Agentic AI for a DeepResearch project

München
Mirza K.

Last position:

Agentic Automation and a RAG system

  • This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.

Used: Python, RAG, LangGraph, LangChain, deepeval, MCP

Verified expert

Songül D.

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Senior SAP Data & Analytics Consultant & Project Manager (BI / BW/4HANA) | Planning (BPC, BI-IP)

Düsseldorf
Songül D.

Last position:

Freelance SAP BW Consultant at DKV Mobility Services

  • Designed and implemented enhancements in SAP BW on HANA 7.5 in the context of CRM migration and S/4HANA and BW/4HANA transformation programs
  • Migrated SAPI data sources to the ODP framework as part of the S/4HANA migration
  • Delivered SAP ECC data to Snowflake using BW data models and Calculation Views for Power BI analytics
  • Integrated SAP and non-SAP data sources (including MS Dynamics)
  • Improved reporting transparency through consolidated data models
  • Optimized data loading processes for FI-CO data sources, significantly improving load times and system performance
Verified expert

Daryoosh D.

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Enterprise Data & AI Architect

Offenburg
Daryoosh D.

Last position:

FP&A Data & AI Architect at Epta Group

Scope: Embedded as FP&A Data & AI Architect within the Finance function of a major European refrigeration manufacturer, leading the transformation of manual, fragmented financial reporting into an automated, governance-driven intelligence platform. Driving the shift from Excel-based controlling to structured data architecture, Power BI analytics, and AI-assisted financial operations.

Financial Data Integrity & ERP Governance

  • Initiated and led GL vs. subledger reconciliation investigations, identifying and resolving structural mismatches between General Ledger and subledger data that had gone undetected prior to engagement
  • Conducted asset analysis to identify items missing from General Ledger postings, surfacing gaps in fixed asset tracking and period-end completeness
  • Validated SAP reports, establishing baseline data quality standards for Finance team consumption
  • Established systematic SAP data validation framework ensuring ongoing integrity between ERP postings and downstream reporting outputs

Finance Reporting Transformation

  • Designed and implemented a structured Transformation Project approach for converting manual Finance reports into fully automated processes
  • Created and owns the Data Reporting Audit Log; a centralized tracking system capturing report owners, stakeholders, data sources, manual effort estimates, and automation opportunity scores across the Finance function
  • Mapped the full reporting landscape identifying quick-win automation targets and strategic Power BI migration candidates
  • Actively reducing manual Excel and PowerPoint dependency across FP&A workflows; replacing point-in-time snapshots with live, governed data models

Power BI & Analytics Enablement

  • Introduced and presented Power BI as the strategic reporting platform to Finance leadership, building internal buy-in for the BI transformation roadmap
  • Designed initial Power BI architecture aligned with SAP, Salesforce and Oracle data structures and FP&A reporting requirements
  • Established report ownership, governance documentation, and data lineage standards enabling sustainable self-service analytics across the Finance team

Transformation Infrastructure & Collaboration

  • Configured and deployed Jira as the transformation project management hub, establishing structured sprint workflows, backlog management, and progress visibility for Finance IT initiatives
  • Proposed and initiated a dedicated FP&A Communication & Transformation Hub, a structured cross-functional forum aligning Finance, IT, and business stakeholders around the reporting transformation roadmap
  • Positioned the Finance function as an active driver of data governance and digital transformation within the broader organization

Outcomes

  • GL/subledger reconciliation gaps identified and investigation framework established within first two weeks of engagement
  • Data Reporting Audit Log deployed; first structured inventory of Finance reporting landscape in company history
  • Power BI transformation roadmap presented and approved by Finance leadership
  • Jira-based project governance live; Finance transformation now tracked with full sprint visibility

Technologies: SAP FI/CO · Power BI · DAX · SQL · Excel (advanced) · Power Query (M) · Power Automate · VBA · Jira · Microsoft 365 · SharePoint · Salesforce (Sales Data) · Oracle HCM · Python

Verified expert

Hervé T.

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Data Engineer & MS Fabric Expert

Oberhausen
Hervé T.

Last position:

Senior Data Engineer at Schweizerische Post AG

Tools: Fabric, AWS, dbt, Power BI, SQL, DWH, R, Python

  • Supported customers in implementing an architecture design for extracting and preparing data
  • Planned the design and implementation of the BI and DWH platform
  • Ensured the scalability and performance of the data platform
Verified expert

Alexander Z.

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Senior Data Architect & Data Engineer

Berlin
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.
Verified expert

Philipp G.

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Machine Learning & Data Engineer

München
Philipp G.

Last position:

Data Scientist & ML Engineer at Data-Science Factory GmbH

  • Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
  • Implementation of automated end-to-end cloud processes
  • Development of LLM and NLP models
  • Creation of interactive reports
  • Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Verified expert

Alexander B.

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Senior Data Engineer

Köln
Alexander B.

Last position:

Senior Data Engineer at RWE AG

Architected and maintained data products for renewable energy operations, covering wind turbine, grid-meter, and weather data. Built scalable ETL/ELT pipelines in Azure Databricks using Delta Lake (bronze/silver/gold layers) and processed data in various formats, including structured and semi-structured data. Contributed to a data quality framework supporting table and column documentation, outlier detection, and completeness metrics across all datasets within a data product. In addition, implemented a DORA KPI Databricks dashboard used across all data products. Optimized CI/CD processes in Azure DevOps to streamline deployment across development, test, and production environments.

Technology stack: Azure Databricks, PySpark, SQL, Delta Lake, Unity Catalog, Azure Data Lake, APIs, Dremio, Azure DevOps, YAML, Git, Databricks Workflows, Application Insights, Terraform, OpenAI API, Codex, LLM-assisted workflows

Verified expert

Nenad B.

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Freelance Computer Vision Engineer

Bonn
Nenad B.

Last position:

Safety Video Analytics Project for Airbus at Airbus

  • Developed a real-time video analytics proof-of-concept for deployment on NVIDIA Jetson edge devices.
  • Implemented DeepStream pipelines including object detection, tracking, human pose estimation, face anonymization, and zone intrusion detection.
  • Built a Qt/Python demonstration UI interfacing with the AI pipeline via REST APIs.
Verified expert

Shishir V.

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IT Project Manager

Düsseldorf
Shishir V.

Last position:

IT Project Manager, Scrum Master, IT Application Manager at TATA Consultancy Services

  • Led end-to-end delivery of complex international customer-facing IT and system transformation projects across mobility, retail, life sciences, and product development domains.

  • Managed project opportunities and delivery initiatives from concept development, planning, execution, migration, validation, deployment, and final customer acceptance.

  • Acted as the primary point of contact for project stakeholders, ensuring alignment between customers, business units, suppliers, and delivery teams.

  • Ensured timely project execution through proactive monitoring of milestones, deliverables, dependencies, risks, and resource requirements.

  • Led and coordinated global cross-functional teams of 20+ members across multiple time zones. Served as the central coordination point for project activities, ensuring accountability, milestones, progress tracking, and timely resolution of issues across multiple stakeholders. Managed status reporting with all the stakeholders, providing clear and timely updates to ensure alignment on project goals.

  • Provided functional leadership, mentoring, coaching, and issue resolution support to project team members. Worked closely with senior leadership and executive stakeholders to define project charter and project scope, priorities, milestones, manage dependencies, success criteria and achieve project goals.

  • Collaborated closely with senior leadership, business stakeholders, suppliers, operations teams, and external partners to define project objectives and ensure successful execution.

  • Managed stakeholder communication through executive dashboards, KPIs, governance reviews, steering committee meetings, and project status reporting.

  • Anticipated business needs and provided effective technical solutions in a timely manner to improve business satisfaction.

  • Led and supported sub-project management structures, ensuring consistent execution, reporting, and escalation across complex initiatives.

  • Managed supplier relationships and service providers to ensure compliance with contractual obligations, service levels, timelines, and quality expectations.

  • Supported vendor selection processes, RFP evaluations, delivery governance, cost monitoring, and performance reviews.

  • Controlled project budgets, forecasts, resource allocations, and financial tracking to ensure delivery within approved constraints.

  • Scheduled workshops explaining the business requirement, prioritizing project goals and managing change.

  • Oversaw cloud migrations (Azure/ AWS), database migrations/upgrades, system upgrades, infrastructure & application modernization, data management, legacy decommissioning initiatives, AI solutioning, transition plan of 2000+ applications etc.

  • Coordinated execution, deployment, integration, commissioning, data migration and handover activities to ensure production readiness.

  • Led a cross-functional team proactively managing dependencies and resolving project issues to closure, while maintaining delivery timelines.

  • Proactively managed project risks, dependencies, issues, change requests, and escalations to ensure predictable project outcomes.

  • Ensured compliance with GxP, ITIL, corporate governance standards, validation requirements, and audit controls.

  • Led internal and external audit support activities, validation documentation, CAPA coordination, and regulatory compliance initiatives.

  • Drove process standardization, automation, and governance improvements across project delivery functions.

  • Led application rationalization initiatives resulting in the retirement of more than 50 legacy applications, improving operational efficiency and reducing maintenance costs.

  • Currently driving Generative AI initiatives to automate project documentation, reporting, knowledge management, and delivery processes.

  • Facilitated lessons learned workshops and implemented continuous improvement actions to enhance project execution effectiveness.

Verified expert

Benito E.

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Cloud DevOps Engineer

Paderborn
Benito E.

Last position:

Cloud DevOps Engineer und Cloud Architekt at Energieversorgungsunternehmen (anonymisiert, NDA)

  • Design and build of a fully isolated AWS offline environment with no outbound internet access for running a browser-based business application
  • Design and implementation of a proxy and response service that terminates all external application calls inside the VPC and serves them from locally stored content; identification of the actual communication needs through measurement-based DNS query logging
  • Creation of architecture designs and decision papers including a comparison of options (Application Load Balancer with Lambda and S3, reverse proxy on EC2, private API Gateway) assessed by operational effort, cost, and availability
  • Transfer of the solution and operations documentation previously available only for Azure to an AWS target architecture, including reassignment of all services and operational processes
  • Automated rollout as Infrastructure as Code (Terraform, CloudFormation) with CI deployment via GitHub Actions, plus setup of private DNS zones and an internal certificate chain for operation without internet access
  • Creation of architecture, deployment, and operations documentation and handover to the customer
  • Build-up of a private cloud platform on OpenStack at provider TelemaxX with Terraform, including FortiGate HA clusters, FortiManager, and Kubernetes
  • Introduction of Policy as Code (Open Policy Agent, Conftest) as well as development of MCP servers (Model Context Protocol) to connect AI assistants to operations and project tools

Successes:

  • Made the business application fully operable without internet access for the first time; the cause of the loading error was narrowed down systematically to missing CORS headers after the likely certificate issue was ruled out
  • Fully transferred an existing Azure concept to AWS and replaced the manually created environment with a reproducible, CI-based rollout

Technology stack: AWS (VPC, Application Load Balancer, Lambda, S3, Route 53 private hosted zones and Resolver query logging, IAM, CloudWatch, EC2, CloudFormation), Infrastructure as Code (Terraform, CloudFormation, Remote State), CI/CD (GitHub Actions with OIDC, Azure DevOps Pipelines), OpenStack, FortiGate, FortiManager, Kubernetes, Policy as Code (Open Policy Agent, Conftest), offline and air-gap architectures, PKI & certificates (internal CA, TLS, CRL/OCSP), DNS, network segmentation, Linux, Windows Server, Python, Bash, PowerShell, YAML, JSON, architecture design & decision papers, documentation (Confluence, Markdown), Generative & Agentic AI (Model Context Protocol, Agentic AI Coding Tools)

Verified expert

Jorge M.

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Data Expert

Würzburg
Jorge M.

Last position:

Technical Lead / Fractional CTO at Würth GmbH

I designed and developed an AI-powered multi-tenant platform on Azure that transforms SAP process recordings into technical documentation, presentations and automated tests, processing over 15,000 process recordings for enterprise customers like Würth. I owned the architecture, the production releases and the DevOps setup. I also designed a multi-tenant system with SSO and role-based access on Azure. Implemented an MCP Server with Dynamic OAuth Authentication.

Main Tasks:

  • Sprint planning and feature preparation
  • Design the multi-tenant platform architecture (FastAPI, SQLAlchemy, PostgreSQL row-level security for tenant isolation)
  • Develop AI pipelines with Prefect for transcription (Azure Speech API), document generation and SAP screen-recording analysis (Claude, gpt-4-mini)
  • Design and implement an MCP server to expose tenant knowledge to LLM clients (Claude), with async retrieval and reranking
  • Implement LLM cost tracking, rate limiting and client pooling for Anthropic/OpenAI/Azure OpenAI endpoints
  • Set up CI/CD: Docker images to Azure Container Registry, GitHub Actions, Azure Static Web Apps, Alembic migrations in containers
  • Manage production releases and execute live data migrations for enterprise customers
  • Define engineering standards and architecture patterns for the team

Environment: Azure / Azure Foundry / Python / FastAPI / Prefect / React / PostgreSQL

Discover over 15,000 top freelancers

Statistics of experts using Databricks

Aggregated from the professional profiles of matched freelancers.

Experience

15 years

Databricks experts have 15 years of professional experience on average.

Position duration

2.9 years

Databricks experts stay in a single position for 2.9 years on average.

Positions per freelancer

10

Databricks experts have completed 10 positions on average over the course of their careers.

Top business areas

Information Technology, Business Intelligence, Product Development

Databricks experts have gathered most of their hands-on project experience in Information Technology, Business Intelligence, and Product Development.

Top industries

Information Technology, Professional Services, Automotive

Databricks experts are most in demand in Information Technology, Professional Services, and Automotive.

Certification focus areas

Information Technology, Business Intelligence, Project Management

Databricks experts earn their certifications most often in Information Technology, Business Intelligence, and Project Management.

Bachelor's degree or higher

97%

97% of Databricks experts hold at least a Bachelor's degree.

Master's degree or higher

68%

68% of Databricks experts hold at least a Master's degree.

Doctorate

17%

17% of Databricks experts have a doctorate (PhD).

Certifications per freelancer

3

Databricks experts hold 3 professional certifications on average.

Most common languages

German, English, French

Databricks experts most often speak German, English, and French.

Speak two or more languages

97%

97% of Databricks experts speak two or more languages.

Based on our profile pool as of 26 Sep 2026.

Daily rate distribution

0% 25% 50% 75% 100%
7% of Databricks experts charge less than €400 per day.
39% of Databricks experts charge between €400 and €800 per day.
49% of Databricks experts charge between €800 and €1200 per day.
5% of Databricks experts charge €1200 or more per day.
<€400 €400-​800 €800-​1200 €1200+

The chart shows how the daily rates of experts in this technology 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 using Databricks

Rates are based on recent contracts and do not include FRATCH margin.

1000
750
500
250
Rate comparison chart
Daily rate avg. 776 €

The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.

1000
750
500
250
Rate comparison chart
Median rate 800 €

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 26 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 (82%)
  • Professional Services (47%)
  • Automotive (41%)
  • Banking and Finance (40%)
  • Manufacturing (40%)
  • Retail (34%)
  • Education (30%)
  • Energy (28%)

Please note that freelancers can work across multiple industries, so percentages overlap.

About the technology

Lakehouse foundation

Databricks is a cloud data and AI platform built around the lakehouse model. It combines scalable data storage with data engineering, analytics, governance and machine learning in one environment. Teams use it to turn raw data into trusted products without maintaining separate systems for every workload.

Core workloads

Databricks specialists support projects such as:

  • Designing batch and streaming data pipelines
  • Building Delta Lake tables and medallion architectures
  • Creating SQL warehouses, dashboards and reporting layers
  • Developing machine learning workflows with MLflow
  • Preparing data for generative AI and retrieval applications

Ecosystem and tooling

The platform connects with cloud storage, databases, business applications and orchestration tools across AWS, Microsoft Azure and Google Cloud. Strong professionals work with Apache Spark, Delta Lake, Unity Catalog, Databricks SQL, notebooks, workflows and the Lakeflow tooling family. They also understand Python, SQL, Scala and common cloud security patterns.

When expertise matters

Companies often bring in freelance Databricks expertise during a migration from legacy warehouses, a lakehouse rollout or a major data quality initiative. Specialists can also help when pipelines are slow, access rules are unclear, cloud costs are difficult to control or machine learning work has not reached production. In distributed teams, remote delivery works well when documentation, workspace access and ownership are clearly defined.

Delivery and governance

A capable Databricks professional starts with data contracts, source-system constraints and business outcomes. They establish reliable ingestion, incremental processing, testing and observability before expanding the platform. Unity Catalog permissions, lineage, workspace structure and deployment practices should support both compliance and productive collaboration.

Signs of strong specialists

Look for professionals who can explain trade-offs rather than only configure notebooks. Useful evidence includes:

  • Production Delta Lake migrations with clear rollback plans
  • Streaming and batch designs matched to actual data needs
  • Practical Spark tuning and workload cost control
  • Secure Unity Catalog and cloud integration experience
  • Reproducible MLflow or model-serving workflows

The best fit combines hands-on platform knowledge with clear communication across data, security and business teams.

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Frequently asked questions

The facts hiring teams ask for most often when it comes to Databricks.

Databricks is used to ingest, transform, govern and analyze data at scale, while also supporting machine learning and AI workflows. Companies commonly use it for lakehouse platforms, streaming pipelines, BI preparation, feature engineering and production model operations.

Databricks emphasizes open data processing, Spark-based engineering and a lakehouse architecture, while Snowflake and BigQuery are often chosen first for managed cloud warehousing and SQL analytics. The right choice depends on data formats, processing patterns, governance needs, existing cloud services and the skills already available in the team.

Databricks work usually benefits from strong Python or SQL skills, Apache Spark knowledge and experience with cloud storage and identity management. Familiarity with Delta Lake, Unity Catalog, orchestration, data quality, Terraform and MLflow can be important depending on the project.

Databricks expertise should match the risk and scope of the assignment rather than a fixed career duration. A specialist handling a migration, security model or production streaming platform should show comparable delivery experience, while a focused SQL or pipeline task may need a narrower skill set.

Databricks projects are often suitable for remote collaboration because work happens in shared cloud workspaces, repositories and documentation. Teams still need secure access, clear ownership, agreed review practices and overlapping communication hours; on-site work can help when source systems or stakeholders require close coordination.

Databricks specialists should explain why they chose a storage layout, processing pattern or governance approach, not only demonstrate notebook syntax. Ask for examples involving testing, observability, failure recovery, access control and production handover, and review whether their recommendations fit your data volumes and operating model.

Databricks supports analytics and machine learning on shared data foundations, which can reduce handoffs between data preparation and model work. Its value depends on disciplined data governance, reproducible experiments and deployment practices rather than simply enabling notebooks.

Databricks freelancers should be comfortable moving between platform configuration, data engineering and stakeholder discussions. They may work with Spark jobs, Delta Lake, SQL warehouses, Unity Catalog, workflows and MLflow, so documenting decisions and making pipelines maintainable is as important as writing code.

The average hourly rate of freelancers who have used Databricks in their recent projects is 97 €, which corresponds to a daily rate of about 776 € based on an 8-hour working day.

Of the freelancers who have used Databricks in their recent projects, 97% hold at least a Bachelor's degree, 68% hold at least a Master's degree, and 17% hold a doctorate.

On average, freelancers who have used Databricks in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2.9 years.

The most common languages among freelancers who have used Databricks in their recent projects are German (99%), English (96%), and French (19%).

The most common industries among freelancers who have used Databricks in their recent projects are Information Technology (82%), Professional Services (47%), and Automotive (41%).

The most common business areas among freelancers who have used Databricks in their recent projects are Information Technology (96%), Business Intelligence (86%), and Product Development (70%).

Main locations of FRATCH Experts, who have recently used Databricks

Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city 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

In Austria our freelancers and interim experts support companies from Vienna to Graz — on-site where your project needs them, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.

Vienna Graz

Across Switzerland our specialists are active in Zurich, Geneva, Basel and Bern — working on-site or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.

Zurich Geneva Basel Bern

Countries:

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