Snowflake Experts in Germany
to build trusted data platforms with vetted, available freelancers matched by AIHire experts who design Snowflake data platforms, build dbt transformation pipelines and connect cloud sources through reliable ELT workflows. Find vetted, available freelancers whose skills match your project precisely and quickly.
Meet FRATCH Experts in Germany, who have recently used Snowflake
Florian R.
Last position:
Global Programme Lead, Ecosystem Separation at Merck Group
Electronics – Surface Solutions | Carve-out of IT, data and system landscape*
Separation of a full business division's IT, data and system landscape following divestment; centrally governed investment envelope in the three-digit m€ range
Laboratory notebook and laboratory analytics platform stream: Palantir Foundry, Snowflake, Signals Notebook, Power BI and connected laboratory instrumentation; site transition completed without interruption to laboratory operations
Decision authority and escalation point for business, IT infrastructure, IT security and vendors; separation executed across globally distributed users and systems, handed over on schedule
Ali A.
Last position:
Founder & Architect at Independent AI R&D
- Fully on-premises LLM document-examination platform for a compliance-critical banking domain: agentic LangGraph pipeline with deterministic verification, every AI judgment structured and source-anchored; ~960 automated tests, zero data egress
- GPU throughput engineering (quantized serving, speculative decoding, prefix caching): 9.5x extraction speed-up, 500+ multi-document case files per day on a single A100
- AI-native EDI/EDIFACT integration platform (~116k LOC Java 25 / Spring Boot 4, 1,900+ tests): LLM-drafted partner mappings machine-verified before go-live (DFDL conformance, field-coverage checks, dry runs), ~99.5% byte match on real customer files — replacing weeks of manual mapping per partner
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
Florian B.
Last position:
Project Management / PMO Consultant
PMO & Project Organization Blueprint for Restructuring
Renewable Energy / Solar Equipment
Technologies / Methods: PMO setup, KPI tracking, project organization, Jira, Confluence
- Developed measures to improve management control during a restruct...
Bardiya B.
Last position:
Data Scientist at Rewe Digital GmbH
Statistical Forecasting Algorithm
- Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
- Migration from R/On-premise to Python/Snowflake
- Productionalization on Snowflake in cooperation with data engineers & DevOps
Monitoring Dashboard
- Data engineering for preparation & provisioning of necessary data/resources on Snowflake
- Development & deployment of a Streamlit dashboard in Snowflake
ML-based Probabilistic Forecasting on Vertex AI
- Development of a ML-based probabilistic forecasting algorithm from scratch
- Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI
Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow
Ajay Kumar D.
Last position:
Senior BI and Analytics Engineer at Novartis
- Led enterprise reporting modernization by migrating legacy SSRS reporting solutions to Power BI, supporting 500+ business users while ensuring full GDPR/DSGVO compliance.
- Designed and optimized Power BI and Microsoft Fabric semantic models using star schema, dimensional modeling, advanced DAX, and performance optimization techniques, reducing query latency by 25%.
- Delivered 20+ executive and operational dashboards featuring KPI scorecards, drill-through, bookmarks, and row-level security, improving reporting efficiency by 20%.
- Enabled self-service analytics through governed Power BI datasets, dataflows, and gateway architecture, increasing business-led reporting adoption by 35%.
- Configured an incremental refresh policy and query folding for a 50+ million row sales dataset, reducing daily report refresh times by 85%.
- Deployed automated ETL/ELT pipelines using Azure Data Factory, Microsoft Fabric, and Snowflake, reducing reporting delivery timelines by 40% through workflow automation.
- Spearheaded Microsoft Fabric analytics modernization initiatives including lakehouse architecture, OneLake integration, and centralized data platform development, reducing data latency from 2 hours to 20 minutes.
- Translated business requirements from 15+ stakeholders into scalable Power BI semantic models and dashboards, improving reporting consistency and reducing ad-hoc reporting requests by 25%.
- Applied Microsoft Copilot and generative AI tools to accelerate SQL development, DAX authoring, technical documentation, and testing activities, reducing development effort by approximately 15 hours per week.
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
Justina K.
Last position:
Freelance Consultant for Change & Data Transformation at Freelance Fast Data Consulting
Project, Strategic Consulting – building the Data Strategy and Data Governance Policy for the German branch, client (private bank Julius Bär, headquarters Zurich), March 2026 – present
- Design and negotiation of the data strategy with key stakeholders, including obtaining board sign-off (strategic consulting) – in this context, regulatory advice on data regulations in the EU and specifically for Germany. The data strategy includes: Data Lifecycle Management: data capture, data storage, data usage, data retention policy, data quality incident management
- Definition of milestones and technical feasibility for implementing TOM for the data strategy, data quality checks, metrics, and a metadata inventory to ensure the bank’s compliance with DORA, BCBS239, and MaRisk requirements.
Core project data change, client: (ING Bank, Frankfurt am Main), March – December 2025
- Concept development and solution design for new end-to-end processes including technical interfaces
- Definition of synchronization logic and data flows between legacy and target systems (decommissioning of legacy systems)
- Analysis and validation of data models
- Stakeholder communication with product owners, feature engineers, UX designers, and operational teams for decision-making
- Analytics and impact assessments, e.g. to assess downstream effects and regulatory requirements
- Documentation and comments on technical and business requirements to support implementation in agile squads
Project digitalization of a user group, client: (ING Bank, Frankfurt am Main), as Interim Product Owner, Jan 2025 – present
- Co-shaping key decisions on data architecture and process logic in the context of historized data and user login functionality
- Development of business solution concepts for migration to the target system, including system integration and data flows
- Support with analytics and impact analyses, especially regarding the ability to provide information to law enforcement authorities
- Active coordination with stakeholders from different squads to support decision-making and ensure regulatory requirements are met
- Creation of test scenarios for operational teams and backend systems in the area of API management using Postman and Bruno.
Saman S.
Last position:
AI Product Builder at Instalemon.com
- Architected and built an agentic creative automation platform on Mastra, with a custom RAG pipeline, custom hooks, tools and skills, Chroma for vector storage, and a MongoDB/Express backend.
- Built the agent orchestration layer powering Pixomi's multi-agent workspace, including 72 custom marketing skills, tools and hooks, and a custom context-management pipeline.
- Designed and implemented evals and observability through Mastra studio.
- Onboarded 10 pilot SMB customers producing 10x publish-ready creative output per campaign versus manual production in 3 months.
- Ran customer discovery and pilot feedback loops to shape the roadmap for an AI-native, workflow-based creation platform.
Laurin H.
Last position:
Software Architect (Freelance) at Care4Sure
- Delivered MVP-focused full-stack architecture for a health-sector client: Vite/React frontend, backend services on Google Cloud Run, and Supabase for database plus IAM/authentication.
- Supported product requirements engineering and prioritized cost-aware workload placement, implementing browser-side/edge computation where feasible before moving logic to backend services.
Sander O.
Last position:
Interim Head of Contract Management at Enpal
- Directed the contracts management function for Europe's largest renewable energy portfolio, 10 FTE
- Implemented process automations by architecting scalable, data-driven workflows leveraging AI and traditional automation
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
Anjali K.
Last position:
Data Analyst / Marketing Automation Engineer at Newfold Digital
- Developed and maintained dynamic 5 Power BI dashboards to report KPIs, identify trends, and uncover actionable insights.
- Collaborated with Marketing and Engineering teams to optimize and implement new marketing campaigns.
- Interpreted data to identify trends and actionable insights, collaborating with stakeholders to convert them to actions and optimisations
- Continuous improvement of reports to enhance understanding of business
Discover over 15,000 top freelancers
Statistics of experts using Snowflake
Aggregated from the professional profiles of matched freelancers.
Experience
16 years
Position duration
3.1 years
Positions per freelancer
10
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Professional Services, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
96%
Master's degree or higher
60%
Doctorate
8%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
98%
Based on our profile pool as of 9 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Germany 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.
Discover detailed Snowflake rate benchmarks:
Explore rate insightsAverage rates of experts in Germany using Snowflake
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 9 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Snowflake 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 (51%)
- Banking and Finance (47%)
- Retail (43%)
- Healthcare (37%)
- Automotive (32%)
- Manufacturing (32%)
- Energy (28%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Cloud data foundation
Snowflake is a cloud data platform for storing, processing and sharing structured and semi-structured data. Its separation of storage and compute supports analytics, reporting, data science and operational workloads without managing traditional warehouse infrastructure. Snowflake runs across major cloud environments and uses SQL as its core working language.
Warehousing and sharing
Companies use Snowflake to consolidate data from business applications, databases, files and event streams. Professionals create governed data warehouses, lakehouse-style architectures, secure data shares and analytics layers for finance, sales, supply chain, marketing and product teams. Data clean rooms and Snowflake Marketplace extend controlled collaboration beyond one organisation.
Ecosystem and tooling
Snowflake projects often depend on a wider data stack:
- dbt for tested, modular SQL transformations
- Fivetran, Airbyte or custom pipelines for ingestion
- Kafka and Snowpipe for event-driven loading
- Power BI, Tableau or Looker for business reporting
- Terraform and Git-based workflows for repeatable environments
Strong specialists understand how these tools affect cost, reliability, lineage and access control rather than treating Snowflake as an isolated database.
When expertise matters
Freelance expertise is useful when a company is migrating from an on-premises warehouse, replacing fragmented reporting or preparing a new data product. It also helps when pipelines are slow, warehouse spend is difficult to explain or teams lack clear ownership of models and data quality. In Germany, specialists may support distributed teams across manufacturing, retail, finance and logistics while adapting collaboration to remote or on-site requirements.
Delivery and governance
A capable professional turns business questions into a practical data model and delivery plan. They configure roles, warehouses, resource monitors, masking policies and retention settings, then establish tests, documentation and observability. They can also tune SQL, choose suitable loading patterns and manage changes through version control without weakening governance.
Choosing a specialist
Look for evidence of complete Snowflake deliveries, not only familiarity with the interface. Ask how the professional handled incremental models, failed loads, schema changes, access boundaries and workload isolation. Strong specialists explain trade-offs clearly, measure data quality with useful checks and leave behind maintainable models, runbooks and ownership practices that the internal team can continue using.
Frequently asked questions
Everything clients usually want to know about Snowflake, in one place.
Snowflake is used to centralise data for analytics, reporting, data science and controlled data sharing. Companies also use it for data products, secure collaboration and workloads that combine structured records with semi-structured files such as JSON.
Snowflake is often compared with BigQuery, Amazon Redshift, Databricks and Microsoft Fabric. Its separate storage and compute model, cross-cloud availability, sharing features and SQL-focused workflow can be strong advantages, while existing cloud commitments, streaming needs and engineering preferences may favour another option.
A strong Snowflake specialist usually works with SQL, data modelling, ELT, dbt and orchestration tools. Experience with cloud security, Terraform, Git, Python, BI tools and observability is also valuable because warehouse work depends on the surrounding pipeline and governance design.
The right level for Snowflake depends on the scope and risk of the work. A focused model or dashboard may suit a specialist with a narrow delivery brief, while a migration, security redesign or enterprise platform needs someone who has handled architecture, production incidents and stakeholder decisions.
Yes, Snowflake work is well suited to remote collaboration because environments, SQL models and infrastructure changes can be reviewed online. German companies should still agree on working language, access procedures, meeting overlap and any need for on-site workshops before the engagement starts.
Bring in a Snowflake freelancer when an internal team needs migration capacity, a faster route to reliable reporting or help resolving pipeline and cost issues. External expertise is especially useful when the company wants a clear architecture and handover without committing to a permanent specialist.
Review how a Snowflake professional approaches modelling, tests, permissions, failure recovery and documentation. Ask for a walkthrough of trade-offs and a sample delivery plan, then check whether the proposed design is understandable, observable and maintainable by the people who will own it.
A Snowflake migration plan should cover source assessment, target models, ingestion, validation, permissions, cutover and rollback. It should also address SQL compatibility, historical data, workload sizing, pipeline monitoring and training so the move improves operations rather than only relocating storage.
The average hourly rate of freelancers in Germany who have used Snowflake in their recent projects is 98 €, which corresponds to a daily rate of about 782 € based on an 8-hour working day.
Of the freelancers in Germany who have used Snowflake in their recent projects, 96% hold at least a Bachelor's degree, 60% hold at least a Master's degree, and 8% hold a doctorate.
On average, freelancers in Germany who have used Snowflake in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 3.1 years.
The most common languages among freelancers in Germany who have used Snowflake in their recent projects are German (97%), English (97%), and French (18%).
The most common industries among freelancers in Germany who have used Snowflake in their recent projects are Information Technology (82%), Professional Services (51%), and Banking and Finance (47%).
The most common business areas among freelancers in Germany who have used Snowflake in their recent projects are Information Technology (95%), Business Intelligence (88%), and Product Development (69%).
Main locations of FRATCH Experts, who have recently used Snowflake
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin 
Hamburg 
Munich 
Frankfurt Request a free demo
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Philipp Thomaschewski
FRATCH CEO
