Google BigQuery Experts in Frankfurt
for fast, reliable analytics with vetted and available freelancersHire experts who design BigQuery data warehouses, optimize SQL and create reliable pipelines with Google Cloud services such as Dataflow and Looker. Get precisely matched with vetted, available freelancers who can start quickly.
Meet FRATCH Experts in Frankfurt, who have recently used Google BigQuery
Torsten F.
Last position:
Data Analyst, Requirements Manager at IsabellenhĂĽtte Heusler GmbH
Analysis of the existing reporting platform, including processes and governance topics, with stakeholders from Sales and Marketing.
Detailed analysis and evaluation of customer-defined requirements for existing reporting and new dashboards to be established.
Support for stakeholders in managing sales processes and identifying KPI trends at an early stage.
Use of Microsoft Power BI as the central analysis and reporting platform.
Development of a proposal for the required further development of processes and the Power BI platform.
Documentation of AS-IS business processes and derivation of company-wide KPIs, including coordination with stakeholders.
Analysis and assessment of the customer's Power BI platform.
Analysis of processes and data governance.
Documentation of AS-IS business processes.
Development of recommendations for further developing processes and the reporting platform.
Design and implementation of dashboards in Power BI.
Development of company-wide KPIs and coordination with stakeholders.
Microsoft Power BI.
Data analytics.
KPI definition and reporting.
Dashboard design and data visualization.
Stakeholder management and requirements management.
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
Carlbandro E.
Last position:
IT Lecturer / Coach at Freelance
As a freelance IT lecturer and IT coach, I specialize in teaching individuals concepts like Cloud Computing, Business Intelligence, Python Programming and Agile Frameworks. My goal is to simplify complex technical concepts into practical, actionable knowledge. Through my extensive experience as an IT consultant in various sectors and roles I know about the importance of IT training - for companies and employees alike.
Polina S.
Last position:
Data Migration Lead – Process Automation, Data Engineering & Reporting at Large Public-Sector Bank
Configured and automated data extracts from Oracle databases, achieving 100% data accuracy in a critical migration project, significantly reducing manual errors and accelerating the migration timeline.
Designed and implemented interfaces with Order Management Systems (OMS), enabling seamless and automated data exchange and improving operational efficiency through faster, error-free order processing across business units.
Developed and deployed data extraction workflows to support regulatory compliance and customer reporting, ensuring timely delivery of key reports, reducing manual effort, and increasing customer satisfaction.
Judith B.
Last position:
BI & Analytics Consulting
- Analysis of web and campaign performance to derive actionable insights for marketing and growth optimization
- Implementation and maintenance of tag management solutions to ensure reliable and consistent data collection
- Continuous development and optimization of reporting structures with a focus on scalability and data quality
- Conducting regular deep-dive analyses and leading monthly stakeholder sessions to present results and align on optimization measures
- Design and operation of end-to-end data flows from data collection to visualization
- Tools: GA4, Google Tag Manager, Looker Studio, Airbyte, BigQuery
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.
Garima C.
Last position:
IT Program Director at Trax Retail
- Directed global digital transformation programs enabling enterprise adoption of AI-powered retail analytics solutions
- Led migration from legacy platforms to cloud-based ecosystems, improving data processing performance by over 40%
- Served as strategic technology advisor to enterprise customers, aligning technology roadmaps with business objectives
- Managed cross-functional global teams of 100+ resources across multiple regions
Jens D.
Last position:
Product Owner & Senior Data Scientist at Legal Tech
- Led an international team of six developers in a Scrum environment
- Defined strategic goals for the project in coordination with stakeholders and the development team
- Prompt engineering for language models to improve the accuracy and relevance of generated responses
- Implemented LangChain components for a RAG chatbot to answer legal questions
- Technologies: GPT-4, LangChain, Python (Pandas, sklearn, streamlit), Docker, GitLab, ChromaDB
Aparna V.
Last position:
Data Manager at University of Cologne
- Engineered and automated a data pipeline using GitLab CI/CD for data ingestion, validation, and loading into a central database.
- Developed Python scripts for data validation and transformation, ensuring data quality and compliance with metadata standards.
- Managed the entire data lifecycle from file-based repositories to a structured SQL Server database.
- Worked in an interdisciplinary team to establish a central database for-omics data and ensure reproducibility of computational analyses.
Discover over 15,000 top freelancers
Statistics of experts using Google BigQuery
Aggregated from the professional profiles of matched freelancers.
Experience
16 years (Germany: 13 years)
Position duration
2.2 years (Germany: 2.1 years)
Positions per freelancer
9
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Banking and Finance, Information Technology, Professional Services
Certification focus areas
Information Technology, Project Management, Business Intelligence
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
75% (Germany: 69%)
Doctorate
38% (Germany: 8%)
Certifications per freelancer
5 (Germany: 3)
Most common languages
English, German, Spanish
Speak two or more languages
89% (Germany: 94%)
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Frankfurt 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 in Frankfurt using Google BigQuery
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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Google BigQuery experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Banking and Finance (67%)
- Information Technology (67%)
- Professional Services (67%)
- Manufacturing (44%)
- Retail (44%)
- Advertising (33%)
- Healthcare (33%)
- Telecommunication (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What BigQuery does
Google BigQuery is a fully managed, serverless data warehouse for large-scale analytics. It lets teams query structured and semi-structured data with SQL without maintaining database servers. Companies use it for reporting, dashboards, product analytics, forecasting and machine learning workloads across Google Cloud.
Core project work
BigQuery specialists turn raw data into governed, usable insight. Typical deliverables include:
- Designing datasets, tables, views and partitioning strategies
- Building ELT pipelines from applications, files and event streams
- Writing and tuning analytical SQL for recurring workloads
- Connecting curated data to Looker and other reporting tools
- Creating BigQuery ML models for practical prediction tasks
Google Cloud ecosystem
Strong work with BigQuery often involves Cloud Storage, Pub/Sub, Dataflow and Dataplex. Professionals may also use Composer for orchestration, Dataform for SQL-based transformations, and Google Cloud IAM for access control. Familiarity with Python, dbt, Terraform and Git helps connect warehouse work with wider delivery practices.
When companies need help
Freelance expertise is useful during a migration from an on-premises warehouse, a new analytics build or a major data quality initiative. It can also support teams when query costs rise, pipelines become difficult to trace or internal specialists lack capacity. In Frankfurt, remote collaboration is common, while workshops with local stakeholders may benefit from on-site availability and clear English or German communication.
What quality looks like
A capable professional starts with data sources, business definitions and expected reporting behavior rather than writing queries immediately. They separate raw, transformed and serving layers, document assumptions, test transformations and apply least-privilege access. They also use partitioning, clustering, query plans and reservations thoughtfully instead of treating performance as a purely technical concern.
Choosing the right specialist
Look for evidence of complete BigQuery delivery, not only isolated SQL examples. Ask how the professional handles schema changes, late-arriving data, sensitive fields, failed loads and reconciliation with source systems. A strong specialist can explain trade-offs between BigQuery, other analytical warehouses and lakehouse patterns in plain language, then leave behind maintainable models, monitoring and documentation.
Frequently asked questions
Not sure where to start with Google BigQuery? These answers cover the essentials.
Google BigQuery is used for analytical workloads such as business reporting, customer behavior analysis, marketing attribution, operational dashboards and machine learning preparation. It is designed for querying large datasets without managing traditional warehouse infrastructure.
Google BigQuery is a serverless warehouse closely integrated with Google Cloud services and usage-based processing. Snowflake and Amazon Redshift may be a better fit when a company prefers their existing cloud ecosystem, commercial model or warehouse operating approach, so the choice depends on data location, governance and workload patterns.
BigQuery work often requires advanced SQL, data modeling and pipeline design. Useful adjacent skills include Python, Dataflow, Pub/Sub, Cloud Storage, Dataform or dbt, Terraform, Looker, IAM and data quality monitoring.
For Google BigQuery, relevant delivery experience matters more than a generic claim of seniority. A specialist should be able to show complete warehouse or migration work, explain cost and performance decisions, and discuss production issues such as schema evolution, failed loads and access control.
Google BigQuery projects are well suited to remote collaboration because development, documentation and cloud environments are accessible online. Teams in Frankfurt may still choose on-site workshops for requirements, governance or stakeholder alignment, with language expectations agreed before work begins.
A strong BigQuery professional can explain the data model, lineage, testing approach and security boundaries without relying on vague claims. Review how they manage partitioning, clustering, query costs, monitoring, documentation and reconciliation against source data.
Google BigQuery supports BigQuery ML, which lets teams create and evaluate selected models using SQL inside the warehouse. More specialized workloads can connect BigQuery with Vertex AI, Python workflows or other Google Cloud machine learning services.
Before starting with Google BigQuery, the freelancer should clarify data sources, ownership, refresh expectations, access policies, retention needs and reporting definitions. They should also confirm the target architecture, existing Google Cloud services, deployment process and how success will be tested.
The average hourly rate of freelancers in Frankfurt, Germany who have used Google BigQuery in their recent projects is 106 €, which corresponds to a daily rate of about 844 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used Google BigQuery in their recent projects, 100% hold at least a Bachelor's degree, 75% hold at least a Master's degree, and 38% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Google BigQuery in their recent projects have 16 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 Google BigQuery in their recent projects are English (100%), German (89%), and Spanish (22%).
The most common industries among freelancers in Frankfurt, Germany who have used Google BigQuery in their recent projects are Banking and Finance (67%), Information Technology (67%), and Professional Services (67%).
The most common business areas among freelancers in Frankfurt, Germany who have used Google BigQuery in their recent projects are Information Technology (100%), Business Intelligence (89%), and Product Development (67%).
Main locations of FRATCH Experts, who have recently used Google BigQuery
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 Cologne 
Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Berlin 
Hamburg 
Munich 
Dusseldorf Countries:
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