Snowflake Experts in Frankfurt
to modernize data platforms with vetted, available freelancers matched in minutesHire experts who design Snowflake data platforms, build reliable ELT pipelines with dbt, and connect cloud data to analytics and machine learning workflows. FRATCH matches you quickly and precisely with vetted, available freelancers.
Meet FRATCH Experts in Frankfurt, 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
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.
Ulm P.
Last position:
DataStage ETL Expert at ING Bank
- Datastage 11.7, dbt, Oracle 19, Python 3.12 / PySpark 3.5, Azure GitHub, Azure DevOps, Automic
- Development of migration jobs to transfer data from the collection DWH to the new Risk Mart, as well as development of ETL pipelines to migrate historical data from the old Mart to the new Risk Mart.
- Storage of the silver layer on Hadoop and the gold layer in Oracle.
- Translation of DataStage jobs into dbt to publish reporting data in Google Cloud to a PostgreSQL database.
- Creation and optimization of complex SQL queries for data extraction from a data vault, taking into account historical data in the point-in-time tables.
- Creation of Oracle table definitions (DDL) and adjustment of existing stored procedures.
- Versioning changes in GitHub and deployment via the CI/CD portal.
- Refactoring long-running DataStage jobs into Python using PySpark to reduce server load.
- Migration of SAS scripts to PL/SQL, including new development of distribution functions that have no direct equivalent in Oracle.
- Development of Automic jobs to run DataStage pipelines and Python scripts (PySpark jobs) that control the population of the SME and institutional risk tables in the Risk Mart and perform business calculations.
- Participation in the agile process, including creating user stories, estimations, and planning in Azure DevOps.
- Handling Azure DevOps tickets and close collaboration with testers and business teams for error analysis and resolution.
Ashkan Z.
Last position:
Microsoft Azure Senior Data Engineer / Senior Data Scientist at Vattenfall Europe
- Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
- Independently designing analytics solutions with Python, SQL, etc.
- Designing and implementing ETLs and data pipelines
- Creating and maintaining APIs
- Independently applying CI/CD, testing, and version control
- Data modeling
- Model development and optimization
- Anomaly detection with AI
- Predictive analytics
Used technologies:
- Snowflake
- Fabric
- Azure Synapse Analytics
- Azure DataFactory
- Azure Data Lake
- Azure DevOps
- Databricks
- Spark
- CI/CD
- SQL Database
- Python
- Power Platform
Kamran V.
Last position:
Agile Delivery Manager at Sparecodes Incorporation
- Highly successful role as lead data analytics expert at a US consulting startup.
- Managed stakeholder expectations by ensuring team delivery velocity using Agile and Lean practices.
- Trained and assembled international offshore data science teams of DevOps engineers, BI developers, and technical support specialists.
- As Sparecodes' data analytics experts, we improved our clients’ business outcomes through requirements management, dashboard and report development, migration, automation, and production support using IBM Cognos, AWS, SAP, Oracle, Teradata, CRM, ERP, Confluence, and Microsoft.
- Provided effective coaching and mentoring for international, multicultural teams in a remote environment, covering new tools and techniques, Agile best practices, internal and external communication, and presentation skills.
- Coordinated technical processes efficiently, including managing and implementing project changes and control processes.
- Promoted strong team engagement and supported employees’ career development, including business change management through coaching.
- Enhanced project status reports by providing insights into team productivity, predictability, and backlog burn-down, leading to improved project outcomes.
- Successfully led the coordination and implementation of software solutions for clients in mechanical engineering, including automation system integration and customization to meet client needs.
Helge B.
Last position:
Solution Architect at Deutsche Bahn
- Responsible for the end-to-end application and integration architecture of the Hamburg S-Bahn maintenance digitization program. A highlight is the introduction of robots and fixed camera towers to automate vehicle inspections, allowing AI image algorithms to assess train conditions during operation.
- Deutsche Bahn has one of the largest SAP PM implementations worldwide, which is a key component of this digitization.
Eduard V.
Last position:
Workshop Leader 'Introduction to AI Development Tools' at Software company in Wiesbaden
- Presentation introducing generic AI and large language models
- Explanation of legal frameworks (EU AI Act, US CLOUD Act, GDPR)
- Systematic review of AI tools along the SDLC and holistic systems
- Comparison of on-prem LLMs vs. cloud-based, as well as change management and works council
- Facilitated the discussion and derived next steps for introducing AI development tools
Leonard H.
Last position:
Freelance Software Engineer & Cloud Architect at Leonard Hußke - IT Solutions
- Evaluation of potential providers (Snowflake vs Databricks) and design of the analytics data platform using Databricks
- Data storage and ingestion layer with Amazon S3
- Creation of ETL processes and data transformations with AWS Glue and Databricks Notebooks
- Orchestration with AWS Glue Workflow, Databricks Workflow and Databricks DLT
- Processing of unstructured data including text, image and video
- Databricks workspace setup and administration
- Setting up a medallion architecture to ensure data quality
- Evaluation of possible BI tools (Power BI, AWS QuickSight, Tableau)
- Establishing MLOps using MLflow
- Introducing data governance and data lineage using Unity Catalog
Roman K.
Last position:
Senior Data Engineer / Cloud Architect at DB Systel
- Development of a central billing app for cloud costs at DB
- AWS
- Python
- AWS CDK
- RDS
- Spark (PySpark)
- Glue
- Lambda
- CI/CD (GitLab)
- React/Typescript
- data optimization
- Scrum
Ritika S.
Last position:
AWmOpsRtKekEX(CPEliRenIEtN: CInEfoSrs.yDs,aHtaitAarcchhiiEtencetr(gAyW) S)
Global marketing analytics for Hitachi Energy as part of a global data modernization initiative aiming to enhance data retention, historical data availability and provide Eloqua's 2-year retention for remote interaction reporting and analytics.
Analyzed Eloqua's default retention policy and identified risk of data loss for records older than two years.
Designed and implemented historical data preservation strategy by creating transformed tables in the target data platform to archive older data while ensuring data quality dashboards.
Collaborated with the Power BI team to re-point dashboards from raw Eloqua imports to the newly created archival layer.
Leveraged Jira to track and manage data engineering tasks, bugs, and feature requests across Agile sprints; coordinated backlog prioritization and task assignment to align data pipeline development with business needs.
Power BI dashboard optimization:
Worked closely with business stakeholders to assess and understand reporting needs for reverse customer data.
Designed and implemented incremental refresh in Power BI to ensure daily updates without full data reloads.
Collaborated with Azure data engineers to optimize data processing and publication pipelines.
Stakeholder communication & data modeling:
Acted as liaison between Group Data Office and Technology Office to align data modelling standards.
Gathered requirements from data engineering team and participated in weekly status meetings to provide implementation updates and resolve blockers across teams in Germany, Poland, and India.
Documentation & quality assurance:
Prepared end-to-end technical design documentation, data flow diagrams, and Power BI audit guides for future reference.
Participated in UAT sessions with business users to validate data outputs and report accuracy.
Markus G.
Last position:
Data Solution Architect, Founder at GRITCON GmbH
- Design and development of modern cloud DWH & data platforms
- Data Vault automation
- Implementation of ELT and CI/CD processes
- Requirements analysis and data modeling
- Building an automated cloud data platform as a reference architecture for financial risk controlling (Snowflake, Data Vault, DBT, Python, GitHub) 2024-10-01 – 2025-06-30, Zurich
- DWH further development, operations and cloud migration (Data Vault, DBT, SAP Data Services, Alteryx, SQL Server, Azure Synapse) 2023-03-01 – 2025-06-30, Frankfurt
- Implementation of a global cloud data platform (Data Vault, WhereScape, Snowflake, AWS, Scrum) 2021-04-01 – 2023-12-31, Cologne
- Proof of concept for a global cloud data platform (Data Vault, Snowflake, Synapse, WhereScape, Azure, Scrum) 2022-04-01 – 2022-07-31, Bonn
- Implementation of a cloud data platform (Data Vault, Snowflake, WhereScape, AWS) 2021-07-01 – 2022-04-30, Karlsruhe
- Big data integration of all source systems related to the ITSM process (Data Vault, Snowflake, WhereScape, AWS, Scrum) 2021-01-01 – 2021-05-31, Prague
- Development and operation of a global self-service BI platform to display around 150 corporate KPIs (Data Vault, WhereScape, Postgres, Jenkins, Talend, AWS, Scrum) 2018-11-01 – 2020-12-31, Frankfurt
- Implementation of a DWH for price management and capacity forecasting in long-distance passenger transport (SAP BODS, SQL Server, AWS) 2017-10-01 – 2018-11-30, Frankfurt
- Introduction of SAP BODS and migration of the existing DWH (SAP, BODS, HANA, Oracle, Cognos) 2017-05-01 – 2017-10-31, Rastatt
Discover over 15,000 top freelancers
Statistics of experts using Snowflake
Aggregated from the professional profiles of matched freelancers.
Experience
20 years (Germany: 16 years)
Position duration
2.2 years (Germany: 3.1 years)
Positions per freelancer
11 (Germany: 10)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Healthcare
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
90% (Germany: 96%)
Master's degree or higher
50% (Germany: 58%)
Doctorate
10% (Germany: 7%)
Certifications per freelancer
5 (Germany: 3)
Most common languages
German, English, French
Speak two or more languages
100% (Germany: 98%)
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Frankfurt are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Frankfurt 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 19 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 (92%)
- Banking and Finance (67%)
- Healthcare (50%)
- Energy (42%)
- Transportation (42%)
- Professional Services (42%)
- Telecommunication (42%)
- Insurance (33%)
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, semi-structured and unstructured data. Its separation of storage and compute supports flexible workloads without the infrastructure management associated with traditional warehouses. Companies use it for analytics, reporting, data applications and governed data sharing across teams.
Warehouses and lakehouses
Snowflake supports central data warehouse patterns as well as modern lakehouse designs. Experts model raw data into trusted layers, choose suitable virtual warehouse configurations and manage workloads across development, testing and production. They also work with databases, schemas, views, stages, file formats and Snowpipe for continuous ingestion.
Pipelines and ecosystem
Snowflake projects often connect several tools and services around the core platform:
- Build ELT workflows with dbt, SQL and orchestration tools
- Load events and files from cloud storage, applications and operational systems
- Connect BI tools such as Tableau, Power BI and Looker
- Use Snowpark for Python, Java or Scala data processing
- Apply streams, tasks and dynamic tables to support incremental transformations
When expertise matters
Companies bring in freelance Snowflake specialists when a migration from an on-premises warehouse needs a clear target design, or when a growing platform has become slow, costly or difficult to govern. They can also support new data products, mergers, regional rollouts and delivery gaps in an internal team. In Frankfurt, remote collaboration is common, while some organizations still need on-site workshops with German- and English-speaking professionals.
Governance and performance
Strong professionals combine SQL and dimensional modeling with practical Snowflake administration. They set up role-based access, masking policies, row access policies, tagging, resource monitors and secure data sharing. They investigate query history, improve warehouse usage, manage dependencies and establish deployment practices that keep analytics reliable as data volumes and teams grow.
Choosing a Snowflake professional
Look for evidence of delivered Snowflake environments, not only course certificates. A capable specialist can explain the trade-offs behind the architecture, show how data quality is tested and describe how access, costs and deployments are controlled. Ask how they would handle source-system changes, failed loads, sensitive data and ownership after handover. Experience with cloud storage, CI/CD, dbt and the relevant BI stack is often as important as Snowflake knowledge itself.
Frequently asked questions
Quick answers to the questions that come up most around Snowflake.
Snowflake is used to centralize data for business intelligence, reporting, operational analytics, data sharing and data science. Companies can ingest data from applications, files and event systems, then transform and expose it through SQL, BI tools or data applications.
Snowflake is often compared with BigQuery, Amazon Redshift and Databricks because all can support cloud data workloads. The right choice depends on existing cloud services, workload mix, governance needs, data engineering preferences and whether the team prioritizes warehouse simplicity, open lakehouse patterns or integrated machine learning.
A strong Snowflake specialist usually combines advanced SQL with dimensional modeling, ELT design and cloud storage knowledge. Experience with dbt, Airflow or another orchestrator, CI/CD, Python, data quality testing and tools such as Tableau or Power BI is valuable.
The required experience depends on the scope and risk of the work rather than the product name alone. A focused transformation or dashboard pipeline may need a specialist who can deliver independently, while a migration, security redesign or multi-team platform benefits from proven architecture and production troubleshooting experience with Snowflake.
Snowflake work is often suitable for remote collaboration because design, SQL development, testing and monitoring are performed in cloud environments. On-site sessions in Frankfurt can still help with discovery, stakeholder workshops and access decisions, especially when German- and English-speaking teams need close coordination.
A Snowflake migration should produce a target architecture, source-to-target mappings, repeatable ingestion and transformation workflows, validation checks and a controlled cutover plan. The specialist should also document permissions, operational ownership, rollback options and differences between the old warehouse and the new platform.
Review whether Snowflake work is secure, testable, observable and understandable to the team taking it over. Good signs include clear data contracts, automated quality checks, least-privilege access, documented lineage, sensible warehouse usage and evidence that failures can be diagnosed without guesswork.
Snowpark lets teams use languages such as Python, Java and Scala to process data close to where it is stored in Snowflake. It can support more complex transformations, feature preparation and data applications, while SQL remains a strong choice for clear, set-based warehouse modeling.
The average hourly rate of freelancers in Frankfurt, Germany who have used Snowflake in their recent projects is 103 €, which corresponds to a daily rate of about 822 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used Snowflake in their recent projects, 90% hold at least a Bachelor's degree, 50% hold at least a Master's degree, and 10% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Snowflake in their recent projects have 20 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers in Frankfurt, Germany who have used Snowflake in their recent projects are German (100%), English (100%), and French (17%).
The most common industries among freelancers in Frankfurt, Germany who have used Snowflake in their recent projects are Information Technology (92%), Banking and Finance (67%), and Healthcare (50%).
The most common business areas among freelancers in Frankfurt, Germany who have used Snowflake in their recent projects are Information Technology (100%), Business Intelligence (92%), and Product Development (67%).
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 Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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Philipp Thomaschewski
FRATCH CEO
