Snowflake Experts
matched in minutes from over 15,000 CVsHire experts who design cloud data warehouses, secure data sharing, ELT pipelines and analytics workloads with Snowflake. FRATCH matches you quickly and precisely with vetted, available freelancers who fit your technical needs.
Meet FRATCH Experts 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:
Business Architect — Project Organization Blueprint for Restructuring
Tasks & results:
- Developed measures to improve management steering during a restructuring program (approx. 80 participants)
- Set up a PMO to ensure transparency, reporting and data-driven decisions
- Created an integration template to transfer team s...
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.
Sarvesh L.
Last position:
Data Analytics for Renewable Energy Integration at Harz University
- Created data pipelines and visualization tools to support sustainable energy decision-making
- Developed insights that could optimize renewable energy deployment and grid integration strategies
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
Deepak M.
Last position:
Lead ML Platform Engineer at Billie GmbH
- Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
- Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
- Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
- Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
- Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
- Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
- Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
- Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
- Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
- Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices
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
59%
Doctorate
7%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
98%
Based on our profile pool as of 26 Sep 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 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 26 Sep 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 (50%)
- Banking and Finance (46%)
- Retail (43%)
- Healthcare (37%)
- Automotive (34%)
- Manufacturing (34%)
- Education (28%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Cloud data foundation
Snowflake is a cloud-native 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 database servers. The Snowflake Data Cloud also enables governed collaboration across teams and organizations.
Core workloads
Companies use Snowflake to centralize data from applications, business systems and external sources. Common deliverables include:
- Enterprise data warehouses for finance, sales and operations
- Lakehouse-style environments for mixed data formats
- ELT pipelines and curated data models
- Secure data sharing and data products
- Analytics foundations for machine learning and reporting
Ecosystem and tooling
Snowflake specialists work across SQL, Snowpark and the platform’s virtual warehouses, databases, schemas and stages. They connect ingestion and transformation tools such as Fivetran, dbt, Matillion and Apache Airflow, while BI teams often use Tableau, Power BI or Looker. Cloud storage, Python and orchestration knowledge are valuable when workloads span several services.
When to hire expertise
Freelance expertise helps when a company is moving from a legacy warehouse, consolidating fragmented data or introducing governed self-service analytics. Specialists can shape the account structure, migrate schemas, tune workloads, control access and establish reliable deployment practices. They are also useful for short-term delivery gaps, audits and complex integrations.
Security and governance
Strong Snowflake professionals treat governance as part of the design, not an afterthought. They define roles and privileges, masking policies, row access rules, resource monitors and retention settings. They also understand zero-copy cloning, time travel, secure views and data sharing, applying them without weakening operational control or data quality.
Signs of quality
Look for professionals who explain trade-offs between warehouse sizing, query patterns, caching and cost control in practical terms. They should be able to trace data from ingestion to consumption, document lineage and test transformations. Experience with CI/CD, monitoring, incident response and stakeholder communication indicates that they can own production outcomes, not only write SQL.
Frequently asked questions
Curious about Snowflake? Here are the answers that come up again and again.
Snowflake is used for cloud data warehousing, analytics, data engineering and governed data sharing. Companies use it to combine information from business applications, transform it for reporting and provide reliable data products to internal or external users.
Snowflake separates storage from compute and manages much of the underlying infrastructure for the customer. Compared with traditional on-premises warehouses, it offers more flexible workload scaling and simpler administration, while platforms such as BigQuery, Redshift and Databricks may be preferred for different cloud, lakehouse or processing requirements.
A strong Snowflake specialist usually combines advanced SQL with data modeling, ELT design and cloud fundamentals. Useful adjacent skills include dbt, Python, orchestration with Airflow, ingestion tools, BI platforms, CI/CD and governance.
The right level depends on the scope, not a fixed number of years. A migration or simple reporting layer may need a focused specialist, while platform design, security, cost control and multiple production pipelines call for someone who has handled the full Snowflake lifecycle.
Yes, most Snowflake work can be delivered remotely because environments, documentation and deployment workflows are cloud based. Teams should still agree on access controls, review routines, working hours, communication language and a clear process for handling production changes.
Snowflake can be a strong choice when governed SQL analytics, data sharing and low infrastructure maintenance are central priorities. Databricks may fit better when an organization needs extensive Spark-based processing, notebook workflows or a lakehouse centered on data science and engineering.
Ask the Snowflake professional to explain a complete design from ingestion through consumption. Review how they handle role-based access, data modeling, testing, warehouse sizing, query performance, monitoring, recovery and cost visibility rather than judging SQL examples alone.
A Snowflake freelancer may deliver a target architecture, migrated schemas, ELT pipelines, dbt models, access policies, dashboards or operational documentation. The exact output should be tied to measurable acceptance criteria, tested in a controlled environment and handed over clearly to the internal team.
The average hourly rate of freelancers who have used Snowflake in their recent projects is 98 €, which corresponds to a daily rate of about 787 € based on an 8-hour working day.
Of the freelancers who have used Snowflake in their recent projects, 96% hold at least a Bachelor's degree, 59% hold at least a Master's degree, and 7% hold a doctorate.
On average, freelancers 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 who have used Snowflake in their recent projects are German (97%), English (97%), and French (16%).
The most common industries among freelancers who have used Snowflake in their recent projects are Information Technology (82%), Professional Services (50%), and Banking and Finance (46%).
The most common business areas among freelancers who have used Snowflake in their recent projects are Information Technology (95%), Business Intelligence (89%), 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
