dbt Experts in Munich
, matched in minutes from over 15,000 CVs with the power of AIHire experts who transform raw warehouse data into tested, documented models, establish reliable analytics workflows, and connect dbt with modern data platforms. FRATCH matches you quickly and precisely with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used dbt
Emanuel F.
Last position:
Interim Architect & Data Taskforce at Freelancer / Project Assignments
- Data Engineering: Design and implementation of scalable data pipelines
- Legacy migrations to Microsoft Fabric (Lakehouse, Dataflows Gen2, Pipelines)
- BO Universe migrations to MS Fabric / Semantic Models / Power BI
- Taskforce for data-driven transformation projects involving Azure Fabric / Oracle / MSSQL
Suyash S.
Last position:
Data Analyst - Reporting & Analytics at SIXT SE
- Developed & maintained customer analytical reporting solutions to identify revenue trends, performance drivers, risks & optimization opportunities to ensure data driven decision making across Sales, Finance, Product, Data Engineering & Controlling.
- Defined & analyzed customer trends & performance metrics to identify root causes behind variances, anomalies & emerging risks across business domains to deliver actionable recommendations.
- Developed & owned analytical data models & reporting layers to ensure scalability, performance & analytical robustness to support executive & operational reporting across business domains.
- Planned, tracked & executed projects by ensuring adherence to timelines, data accuracy, consistency, deliverables, reliability & data quality standards through rigorous validation & reconciliation processes.
- Raised the analytical maturity by formalizing analytical workflows, documenting data processes & standard operating procedures (SOPs) & conducting training sessions to drive adoption of self-service analytics & embed a data driven culture across operational and business teams.
- Took ownership of the end-to-end lifecycle roadmap from requirement gathering, collection, transformation, developing robust business logics to data storytelling & stakeholder delivery.
- Converted complexity into structured clarity by translating requirements & business processes into analytical recommendations to ensure alignment between non-technical & technical stakeholders.
- Conducted advanced SQL based analysis of complex business datasets to uncover trends, correlations & performance improvement opportunities.
- Drove process automation & efficiency improvements by leveraging Python, SQL optimization & AI assisted tools to reduce processing time & increase reliability across analytical & operational workflows.
- Standardized KPI definitions & reporting logic to ensure consistency & trust across reporting solutions.
- Developed process monitoring dashboards & analyses to identify inefficiencies, bottlenecks & compliance deviations across end-to-end business processes to derive actionable recommendations for process improvement & automation.
Any-Arlene N.
Last position:
Co-Founder · Data Engineering & Backend at zirikana (Kirundi Bible Web App) – Civic Technology
- Built a Python pipeline that converts lectionary web content into structured daily JSON, applying liturgical-calendar rules for accurate weekday and Sunday coverage.
- Shipped a read-only FastAPI REST API with shared Pydantic models and delivered a Kirundi-first web client for browser and mobile use.
- Owned the data layer and backend architecture, collaborating closely on system architecture and interfaces while automating refreshes with GitHub Actions and validating the ETL with pytest.
- Impact: Created a reliable, API-driven source of truth for daily Bible readings in Kirundi, enabling consistent access to previously unstructured content.
Serge K.
Last position:
MLOps (machine learning operations) at REWE Digital GmbH
- It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
- GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
- Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
- CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
Eli R.
Last position:
Technical co-founder at AskTheLaws
- Create an AI legal assistant with modern ML capabilities.
- Implement RAG architecture, with data pipelines for legal data search.
- Use AWS Bedrock for LLM and embedding models and LangChain/LangGraph
- Python with FastApi for backend and React for frontend
Anitha N.
Last position:
Senior Data Engineer at Accenture GmbH
- Designed, developed, and configured scalable data applications aligned with business processes and technical requirements.
- Architected scalable, cost-effective data architectures leveraging Snowflake across AWS, Azure and GCP, integrating dbt for data transformation and modeling.
- Built and maintained robust ETL Data Pipelines, ensuring high data quality for seamless migration and cross-system integration.
- Demonstrated strong expertise in SQL & Python with extensive experience in data modeling, ETL/ELT pipeline development, and streaming data processing; proficient in Git-based version control, CI/CD practices, and testing frameworks, with solid knowledge of data quality, observability, cost optimization, security, and data governance principles.
- Led multiple data migration initiatives from SAP HANA to Snowflake using a modular dbt framework.
- Designed and maintained end-to-end data transformation workflows using dbt on Snowflake, implemented layered data models, optimized performance, and ensured high-quality data delivery for business intelligence and reporting.
- Managed development, QA, and production deployments through structured version control and release management using GitLab.
- Integrated and centralized data from multiple sources including relational databases, flat files, Excel, and large-scale systems into Snowflake.
- Applied strong expertise in Sales, Marketing, HR, and ERP data domains, developing and maintaining relevant KPIs and reporting solutions.
- Collaborated with cross-functional teams to deliver end-to-end data solutions on schedule through proactive issue resolution and effective coordination.
- Administered the Snowflake sandbox environment for Data Engineering division.
- Trained colleagues transitioning into data roles on Snowflake and provided technical guidance and mentorship to junior team members.
Hardeep B.
Last position:
Sr. Data Engineer at Charles Schwab Bank
- Designed and implemented end-to-end data pipelines (batch & streaming) using Python, SQL, and Apache Spark, Databricks on AWS reducing ETL latency by 40%.
- Developed serverless event-driven ingestion pipelines using AWS Lambda and SQS, ensuring real-time data availability for downstream analytics.
- Leveraged Google Cloud Platform (GCP) services including BigQuery and Dataflow to manage cross-cloud data warehousing and analytics integration.
- Expertise in DMS (CDC, Full Load) and Airflow for scalable data pipeline automation and orchestration.
- Managed and customized data pipelines using Databricks, Airflow. Automation using Docker, Kubernetes, Terraform.
- Automated data quality checks using dbt to modularize transformations and ensure production-grade data lineage, improving reliability by 30%.
- Collaborated with compliance teams to ensure GDPR and SOC2 alignment. Mentored junior engineers and contributed to architecture refactoring for scalability.
- Created and maintained dashboards in Power BI to provide actionable insights.
Christian S.
Last position:
Data-Scientist/AI Engineer at The Marcom Engine GmbH & Co. KG
- Concept creation and implementing AI Agents in AWS Cloud
- Continuously alignment with stakeholders
- Collaborate with DevOps
- Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.
Nima N.
Last position:
Co founding LLM Engineer at LLM Ventures
- Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
- Designed and implemented multi-agent AI workflows for financial and trading applications
- Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
- Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
- Led system architecture decisions across model selection, orchestration, state management, and deployment
Hans L.
Last position:
Requirements Engineer at REWE Systems GmbH
- Gathering requirements from business units and stakeholders
- Organizing and running workshops
- Creating and supporting user stories (from refinement to rollout) and mapping
- Facilitating Scrum ceremonies
- Identifying and analyzing optimization potentials like the REWE Pick&Go app
- Moderation and communication with service providers
- Requirements engineering in connection with external systems
- DWH/BI solution: loyalty reporting with MicroStrategy
- ITIL (framework for IT service delivery)
- Quality assurance (quality gates) based on ISTQB
Martin S.
Last position:
Business Intelligence Data Analyst at webeet
- Optimized SQL data pipelines for clean insights.
- Analyzed and visualized trends with Python.
- Improved dashboards and automations.
- Worked with Google Sheets, GCP, Snowflake, dbt, Spreadsheets/Excel, Databricks, PySpark, and Fivetran.
Manikanta R.
Last position:
Data Engineer at Insurance client
- Design, development, and maintenance of end-to-end ETL pipelines for scalable and reliable data integration
- Support in data quality checks, testing, and migrations
- Development and maintenance of dbt models for structured, modular, and reusable data transformations
- Use of AI-driven development to improve ETL job creation and code quality.
- Development of CI/CD for automated deployment.
Tobias R.
Last position:
Senior Data Scientist at Lyft
- Improved error rate in Speed Limit elements from 24% to 8% by implementing an LLM pipeline on detected objects (with natural lower bound of 6% as image coverage is only 94%).
- Extensive ML modeling of Routing Cost Function (objective function, features, hyperparameters, training data generation) which led to setting the foundation for a rebuild of a more flexible setup.
- Initiated the first Prioritization Framework for Data Curation Ops ($2M annual organizational expenses) which moves away from daily quotas and now optimizes for ‘expected business value per time unit’, achieving around 5-7% efficiency improvement.
- Close collaboration with Software Engineering & Data Engineering as well as Product & Operations.
Discover over 15,000 top freelancers
Statistics of experts using dbt
Aggregated from the professional profiles of matched freelancers.
Experience
17 years (Germany: 14 years)
Position duration
1.8 years
Positions per freelancer
12 (Germany: 10)
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Information Technology, Professional Services, Retail
Certification focus areas
Information Technology, Business Intelligence, Human Resources
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
67% (Germany: 63%)
Doctorate
17% (Germany: 8%)
Certifications per freelancer
2 (Germany: 4)
Most common languages
German, English, French
Speak two or more languages
92% (Germany: 99%)
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 Munich 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 Munich using dbt
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.
dbt 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 (85%)
- Professional Services (54%)
- Retail (54%)
- Banking and Finance (46%)
- Media and Entertainment (46%)
- Insurance (38%)
- Manufacturing (38%)
- Education (31%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Data transformation
dbt is a transformation framework for analytics engineering. It lets teams turn raw warehouse data into modular SQL models that are tested, documented and ready for reporting, dashboards, operational analysis and data products. Processing stays in the warehouse, while transformation logic is managed as code.
Core workflow
Experts use dbt to structure a dependable path from ingestion to trusted data. They define sources, build models with SQL and Jinja, set dependencies, and use the project graph to manage execution.
- Create staging, intermediate and mart models
- Add schema, freshness and custom tests
- Generate documentation and lineage
- Configure incremental models and snapshots
Ecosystem and tooling
The wider dbt ecosystem includes dbt Core, dbt Cloud, adapters and integrations for warehouses such as Snowflake, BigQuery, Databricks, Redshift and Microsoft Fabric. Strong specialists also work with Git, CI pipelines, orchestration tools, semantic layers and ingestion services, adapting the workflow to the company’s existing stack.
When expertise matters
Companies bring in freelance dbt specialists when a warehouse has become difficult to trust, maintain or extend. They may need a new analytics foundation, a migration from legacy SQL, faster delivery of reporting models, or a clear governance approach across several data teams.
- Establish project conventions and reusable macros
- Refactor slow or duplicated transformations
- Introduce testing, deployment and review workflows
- Prepare a warehouse migration or new data domain
Quality signals
A strong professional understands more than SQL syntax. They explain grain, keys, joins and business definitions clearly, design tests that catch meaningful failures, and keep models easy to review. They also know when an incremental strategy, snapshot or macro improves the system and when it adds unnecessary complexity.
Collaboration and delivery
Good dbt work connects technical decisions with the needs of analysts, data scientists and business teams. In Munich, specialists may work on site or remotely with local and international stakeholders; clear documentation, reliable English communication and, where useful, German language skills support smooth delivery. The best engagements leave behind maintainable models, useful lineage and a workflow the internal team can own.
Frequently asked questions
Need clarity? These are the questions we hear most often about dbt.
dbt is used to transform, test and document data inside a cloud or modern analytical warehouse. Companies use it to create dependable datasets for dashboards, reporting, forecasting, experimentation and data products.
dbt focuses mainly on the transformation layer and runs SQL in the target warehouse, rather than extracting and loading data itself. Traditional ETL suites may provide broader visual workflows, while dbt offers code-based version control, modular models, testing and lineage for analytics work.
A strong dbt specialist usually combines advanced SQL with warehouse design, Git, CI/CD and data quality practices. Experience with ingestion tools, orchestration, Python, BI semantics and cloud warehouses is also valuable when the work spans the full analytics stack.
dbt work can range from improving a few models to designing a complete analytics layer, so the required background depends on the scope. For a larger engagement, look for someone who has handled warehouse architecture, model governance, testing strategy and collaboration with business stakeholders.
dbt projects are well suited to remote collaboration because code, reviews, documentation and deployment can be managed online. Teams in Munich should agree on working hours, communication routines and whether German is needed for stakeholder discussions; English is often suitable for technical delivery.
Review how the dbt specialist explains model grain, dependencies, tests and failure handling. Ask for examples of documentation, code review practices and performance decisions, then use a focused technical discussion to see whether the person connects implementation choices to business definitions.
dbt Core is the open-source command-line framework for building and running projects, while dbt Cloud adds managed development, scheduling, job monitoring and collaboration features. The right choice depends on the team’s infrastructure, governance needs and preferred operating model.
A company should consider a dbt expert when transformation logic is duplicated, reporting definitions conflict, tests are missing or warehouse changes are hard to release safely. A freelancer can establish patterns quickly, unblock a migration and transfer practical knowledge to the internal team.
The average hourly rate of freelancers in Munich, Germany who have used dbt in their recent projects is 101 €, which corresponds to a daily rate of about 806 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used dbt in their recent projects, 100% hold at least a Bachelor's degree, 67% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Munich, Germany who have used dbt in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Munich, Germany who have used dbt in their recent projects are German (92%), English (92%), and French (31%).
The most common industries among freelancers in Munich, Germany who have used dbt in their recent projects are Information Technology (85%), Professional Services (54%), and Retail (54%).
The most common business areas among freelancers in Munich, Germany who have used dbt in their recent projects are Business Intelligence (100%), Information Technology (92%), and Product Development (62%).
Main locations of FRATCH Experts, who have recently used dbt
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 Request a free demo
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FRATCH CEO
