Databricks Experts
matched in minutes from over 15,000 CVsHire experts who design lakehouse architectures, develop Apache Spark workloads, and deliver reliable data pipelines with Delta Lake, SQL, and machine learning tools. FRATCH connects you with vetted, available freelancers through fast, precise AI matching.
Meet FRATCH Experts who have recently used Databricks
Dany-Armand D.
Last position:
Senior Data Scientist at ibg NDT GmbH
- Investigate the relationship between Eddy Current Testing (ECT) signals and microstructural properties
- Detect latent patterns in ECT data that reflect intrinsic material characteristics
- Develop and validate predictive models for microstructural classification and quantification, using hardness and case depth as benchmarks
- Apply Bayesian Structural Equation Modeling for advanced data analysis
Max R.
Last position:
Senior AI Engineer and Technical Advisor (Freelancer) at dialpad
Serving as AI technical advisor to Dialpad's Telephony Engineering division while building AI products hands-on. Currently developing MCP-based tooling for AI text and voice agents: agent authoring, integrations and adoption of existing agents, shared authentication and API contracts, and durable workflow state. This includes text and voice tests that evaluate agent behavior, latency and task completion.
In parallel, helping Dialpad's engineers adopt QualityLayer and use Claude Code and Codex effectively. This ongoing work covers context management for large codebases, reusable plan-implement-test-review workflows connected to CI/CD, playbooks, hands-on workshops, labs and pair programming.
Technologies: AI Agent Products (Text & Voice Agents, MCP, LLMs, Python, TypeScript), Agent Tooling (Authoring, Integrations, OAuth, Durable State), Quality & Evaluation (Text & Voice Tests, Evals, Regression, Traces), Agentic Engineering (Claude Code, Codex, QualityLayer, Hooks, Prompts), Context Engineering (RAG, Vector Stores, Codebase Embeddings), Software Delivery (CI/CD Integration, Agentic Loops, PR Automation), Enablement & Coaching (Workshops, Hands-On Labs, Pair Programming)
Peter S.
Last position:
Senior ML Engineer & AI Researcher at Anonymous Client
Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing
- Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
- Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
- Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.
Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision
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.
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.
Michael B.
Last position:
Product Analytics Consultant - Trust & Safety at Kleinanzeigen
- Detecting fraud patterns and implementing effective anti-fraud rules while maintaining acceptable false positive rates to ensure a balance between safety and a frictionless user experience
- Supporting ideation and roll-out of new trust and safety features to the platform to block fraudulent activity and increase user awareness for fraud
- Supporting Product, Development and Customer Support with BI reports and further guidance to identify and fight fraud and policy violations
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.
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
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
Ashwin P.
Last position:
Freelance Data Scientist at Mercor Intelligence
- Architected and deployed end-to-end machine learning pipelines across classification and prediction datasets, ensuring robustness and reproducibility through MLOps best practices.
- Contributed directly to LLM model output accuracy improvement by designing and engineering specialised prompts grounded in end-to-end ML and SciML pipeline logic.
- Developed training data for large language models by formulating coding problems that models could not resolve and subsequently documenting the correct solutions.
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
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
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.
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
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
Discover over 15,000 top freelancers
Statistics of experts using Databricks
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
2.9 years
Positions per freelancer
10
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Professional Services, Automotive
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
98%
Master's degree or higher
69%
Doctorate
16%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
97%
Based on our profile pool as of 16 Oct 2026.
Daily rate distribution
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.
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 16 Oct 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 (41%)
- Manufacturing (40%)
- Retail (35%)
- Education (30%)
- Healthcare (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 lake storage with warehouse-style governance, SQL analytics, streaming, and machine learning in one environment. Teams use it to create trusted data products without maintaining separate systems for every workload.
Core workloads
Databricks supports batch and streaming pipelines, business intelligence datasets, predictive models, and generative AI applications. Delta Lake adds transactional reliability, schema controls, time travel, and data quality features to cloud object storage. Teams can also use notebooks and SQL warehouses for exploration, reporting, and operational analysis.
Ecosystem and tooling
- Apache Spark for distributed processing and transformations
- Delta Lake for reliable tables and lakehouse storage
- Unity Catalog for governance, discovery, and access control
- Structured Streaming for continuous data pipelines
- MLflow and Mosaic AI for model tracking and AI workflows
Databricks connects with cloud services, orchestration tools, BI products, source systems, and software repositories. Strong specialists understand these integrations as well as the platform itself.
When expertise matters
Companies bring in freelance expertise when a lakehouse needs a clear architecture, a migration from legacy warehouses is underway, or pipelines have become slow and difficult to trust. Specialists also help establish governance, optimize Spark jobs, structure workspaces, and prepare data for analytics or machine learning.
- Data quality rules are inconsistent across pipelines
- Cloud storage contains valuable data that teams cannot use reliably
- Streaming workloads need stronger monitoring and recovery
- SQL, notebooks, and machine learning work in disconnected processes
Skills to assess
A capable professional can model data with Delta tables, tune Spark transformations, manage partitioning, and design resilient batch or streaming jobs. Look for experience with cloud security, identity management, infrastructure automation, CI/CD, Python, SQL, and the relevant cloud environment. Clear documentation and practical governance choices matter as much as implementation speed.
Project collaboration
Databricks work can be delivered remotely when access, environments, and ownership are well defined. The engagement still needs close collaboration with data owners, security teams, analysts, and software specialists. Before hiring, define the target workloads, data sources, cloud setup, service-level expectations, and the deliverables that will prove the platform is ready for use.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Databricks.
Databricks is used to build lakehouse platforms for data engineering, analytics, streaming, machine learning, and AI applications. It gives teams shared tools for processing data, managing trusted tables, training models, and serving business intelligence workloads.
Databricks combines data lake flexibility with warehouse-style SQL and governance. Compared with a traditional warehouse, it is often chosen when teams need large-scale data processing, open cloud storage, streaming, and machine learning alongside reporting.
A strong Databricks specialist usually brings Apache Spark, Delta Lake, Python, SQL, cloud storage, and orchestration experience. Useful adjacent skills include Unity Catalog, infrastructure automation, CI/CD, data modeling, monitoring, and MLflow.
The right level of Databricks experience depends on the workload and its risks. A simple pipeline may need focused delivery skills, while a governed lakehouse or production AI system calls for architectural judgment, security knowledge, performance tuning, and operational ownership.
Yes, Databricks projects can often be handled remotely when cloud access, documentation, communication routines, and deployment ownership are clear. On-site collaboration may help during workshops, complex migrations, or discussions involving sensitive data and many internal teams.
Review whether the Databricks solution has clear data contracts, reliable Delta tables, controlled access, observable pipelines, and repeatable deployments. Ask the professional to explain trade-offs around cost, performance, recovery, schema changes, and long-term maintenance.
Databricks supports streaming through Structured Streaming and Delta Lake. It can process continuous events, maintain reliable tables, and combine streaming with batch transformations, but the design still needs careful handling of late data, checkpoints, failures, and downstream consumption.
Before starting a Databricks engagement, clarify the cloud provider, workspace structure, data sources, identity model, deployment process, and expected deliverables. Freelancers should also confirm who owns governance, production support, cost controls, and access to business context.
The average hourly rate of freelancers who have used Databricks in their recent projects is 97 €, which corresponds to a daily rate of about 778 € based on an 8-hour working day.
Of the freelancers who have used Databricks in their recent projects, 98% hold at least a Bachelor's degree, 69% hold at least a Master's degree, and 16% 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 
Dusseldorf 
Nuremberg 
Vienna 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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