Dagster Experts in Germany
matched in minutes by AIHire experts who design asset-based pipelines, integrate dbt and Snowflake, and implement observability with Dagster. Get precise access to vetted, available freelancers selected for your data platform needs.
Meet FRATCH Experts in Germany, who have recently used Dagster
Mansharan K.
Last position:
MS Fabric | Power BI Consultant at Gropyus Technologies GmbH
- Architected an enterprise Fabric-based data-product governance framework, authoring data contracts and schema-validation KPIs to enforce completeness, freshness and SLA standards across reporting data products.
- Operationalized pipeline observability by integrating Dagster orchestration data into automated data-health reporting, proactively validating data integrity and pipeline reliability before production reporting layers refresh.
- Established BI-as-Code and end-to-end governance through Git-based version control, CI/CD and Alpha→GA release management, strengthening lineage, schema-drift detection and production change traceability.
Stephan B.
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
Daniel S.
Last position:
Senior Software Engineer at energielenker solutions GmbH
- Designed and implemented a Python-based ETL pipeline with the Dagster framework to transform raw energy data from heterogeneous sources using InfluxDB and visualizations in Grafana
- Defined time-based and dependency-based jobs
- Deployed to managed Kubernetes clusters using Helm
- Integrated InfluxDB Cloud
- Prepared data for use in Grafana, including cleaning, normalization, and time-based resampling in Python
- Developed dashboards and visualizations in Grafana
- Developed unit tests with mocking using pytest
- Set up a CI/CD pipeline in GitLab
Technologies: Python, Dagster, InfluxDB, Grafana, pandas, pytest, REST, CI/CD, GitLab, Container, Kubernetes, Helm, Docker, Cloud
Santina W.
Last position:
Business Analyst & BI Strategist - Comparison Portal at dataweys (self-employed)
- Assessment of the existing reporting landscape and strategic bundling of needs
- Migration and consolidation of reports to Metabase, connected to ClickHouse as the data foundation
- Building and maintaining data pipelines
Stack: Metabase · ClickHouse · Appsmith · Airflow
Maurizio F.
Last position:
Python Software Developer at Schönhofer Sales and Engineering GmbH
- Implemented a command line interface (CLI) for integration of REST APIs of various microservices for end users
- Centralized and simplified interaction with services through the CLI
- Implemented a REST microservice for custom data schemas based on an API-first approach
- Developed event-driven control with RabbitMQ to connect to other services
- Deployed services using Docker and Kubernetes and extended the CLI
- Managed complexity and data volume handling through the microservice
Can S.
Last position:
Platform Engineer at ClimateChoice
In a lean, execution-focused environment, I took ownership beyond a narrow engineering lane, shaping and implementing systems across backend, data, and infrastructure. Partnered directly with the three founders in a fast-moving, high-stakes environment, turning strategic priorities into concrete technical decisions and production outcomes.
- Owned core platform development across backend (Django/Rest Framework/Postgres), ETL (Python/Dagster), infrastructure (Terraform/Kubernetes/AWS), and frontend (typescript/react) for a climate-tech SaaS product, driving continuous cross-stack development across five repositories from October 2021 to this day.
- Architected and owned a standalone internal Python scoring framework for CRC assessments, using YAML-driven rules and metaprogramming to enable non-technical users to define complex evaluation logic without hardcoded implementations.
- Built and stabilized ETL and scraping pipelines using Dagster and Scrapfly, improving document ingestion, tagging, retry behavior, deployment flow, and operational resilience.
- Contributed to platform modernization and reliability through Django/Python upgrades, Postgres/RDS and EKS changes, CDN/TLS updates, test and performance improvements, and observability hardening.
- Drove backend engineering for product features, translating requirements into technical specifications, API contracts, data structures, and scalable implementation plans.
Jorge M.
Last position:
Data Architect at Deutsche Bahn
- Design and provide best practices on data modeling for dbt, including changing dimensions, late arriving data handling, and testing
- Design the ingestion flow from other systems into S3 and Redshift
- Design and implement new partitions for Dagster and incremental loading with dbt
- Map business requirements to technical architectures
- Instruct junior team members
Axel B.
Last position:
Project lead for introducing Okta as an IAM system at Fritz Schäger GmbH & Co KG
- Project management
- Building an internal IAM team
- Okta
- Project management
- IAM processes
Jonas K.
Last position:
Business Intelligence Team Lead at CHI Deutschland Cargo Handling GmbH
- Built a new BI team, Snowflake data warehouse and BI infrastructure from scratch
- Lead the BI team to drive data-driven decision-making across cargo handling operations.
- Design and maintain enterprise data solutions for performance tracking, forecasting, and operational efficiency.
- Collaborate with stakeholders to align BI strategy with business objectives in a high-throughput logistics environment.
Discover over 15,000 top freelancers
Statistics of experts using Dagster
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
1.6 years
Positions per freelancer
10
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Project Management, Operations
Bachelor's degree or higher
100%
Master's degree or higher
43%
Doctorate
29%
Certifications per freelancer
5
Most common languages
German, English, Spanish
Speak two or more languages
100%
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 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.
Average rates of experts in Germany using Dagster
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.
Dagster 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 (100%)
- Banking and Finance (56%)
- Professional Services (56%)
- Education (33%)
- Energy (33%)
- Insurance (33%)
- Transportation (33%)
- Manufacturing (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Dagster does
Dagster is an orchestration framework for building, scheduling and observing data assets and pipelines. It helps teams define how datasets are produced, validate dependencies and track materializations across development and production. Its software-defined asset model makes data workflows easier to understand and maintain.
Core building blocks
Dagster projects combine assets, jobs, schedules, sensors and resources. Assets describe the data a workflow creates, while jobs select work to run and sensors react to events. Resources connect pipelines to warehouses, storage, APIs and compute systems without hard-coding operational details.
- Define software-defined assets and dependencies
- Configure schedules, sensors and partitions
- Manage environments, resources and run configuration
- Add checks, metadata and lineage visibility
Ecosystem and tooling
Professionals often connect Dagster with dbt, Airflow migrations, Spark, Snowflake, BigQuery, DuckDB and cloud object storage. Python is central to project development, while Dagster integrations support common databases, transformation tools and execution environments. Dagster UI and Dagster+ add visibility, deployment and operational controls.
When companies need expertise
Companies bring in freelance specialists when pipelines have become difficult to test, monitor or change safely. They may be moving from scripts or Airflow, introducing an asset-based model, or standardizing workflows across analytics and machine learning teams. In Germany, remote collaboration is common, with on-site work useful for architecture sessions and stakeholder workshops.
- Modernize fragmented data orchestration
- Establish reliable backfills and partitioning
- Connect warehouse, lake and transformation systems
- Prepare Dagster for production deployment
What strong specialists deliver
Strong professionals translate business data products into clear asset definitions and manageable dependencies. They design idempotent operations, sensible partition strategies and useful failure handling. They also document ownership, test transformations and make pipeline behavior understandable to data consumers and operations teams.
Choosing the right professional
Look for practical work with Dagster repositories, deployment workflows and the systems in your stack. Ask how the specialist handles retries, backfills, sensors, secrets, environments and observability. A good match can explain trade-offs with Airflow or Prefect, communicate clearly with analysts and platform teams, and leave behind maintainable code rather than a collection of opaque jobs.
Frequently asked questions
What clients ask us most about Dagster — answered in short.
Dagster is used to orchestrate data assets and pipelines from ingestion through transformation and delivery. Companies use it to schedule work, manage dependencies, monitor runs, support partitioned data and make lineage visible across warehouses, lakes and machine learning workflows.
Dagster emphasizes software-defined assets, data lineage and typed, testable pipeline definitions. Airflow is widely used for task-oriented scheduling and has a broad operator ecosystem, so the better choice depends on whether the project needs an asset-centric model, existing Airflow knowledge or specific integrations.
Dagster and Prefect both provide Python-based orchestration, but they encourage different ways of modeling workflows and operational metadata. Dagster is often selected for asset lineage and structured data products, while Prefect can suit teams that prefer a more flexible flow and task model.
A strong Dagster specialist should understand Python, SQL, data modeling and testing. Experience with dbt, Snowflake, BigQuery, Spark, Docker, Kubernetes, cloud storage and CI/CD is valuable when the orchestration layer must operate as part of a wider data platform.
The right Dagster background depends on the scope. A focused pipeline integration may need a specialist who can work within an existing repository, while a platform migration or production rollout calls for experience with architecture, deployment, backfills, observability and team enablement.
Dagster work is well suited to remote collaboration because repositories, deployment configuration and run history can be reviewed online. For teams in Germany, clarify working hours, documentation standards and German or English communication needs before the engagement begins; on-site workshops can still help with architecture decisions.
Ask a Dagster specialist to explain a real asset graph, including partitioning, retries, sensors, resources and failure recovery. Review code quality, tests, deployment practices and documentation, and check whether the person can connect orchestration choices to data ownership and business requirements.
Introducing Dagster can expose unclear ownership, inconsistent schemas and pipelines that are not idempotent. Teams also need to plan deployment environments, secrets, run storage, alerting, backfills and the migration of existing scripts or Airflow workflows without disrupting data delivery.
The average hourly rate of freelancers in Germany who have used Dagster in their recent projects is 99 €, which corresponds to a daily rate of about 789 € based on an 8-hour working day.
Of the freelancers in Germany who have used Dagster in their recent projects, 100% hold at least a Bachelor's degree, 43% hold at least a Master's degree, and 29% hold a doctorate.
On average, freelancers in Germany who have used Dagster in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Germany who have used Dagster in their recent projects are German (100%), English (100%), and Spanish (11%).
The most common industries among freelancers in Germany who have used Dagster in their recent projects are Information Technology (100%), Banking and Finance (56%), and Professional Services (56%).
The most common business areas among freelancers in Germany who have used Dagster in their recent projects are Information Technology (100%), Business Intelligence (78%), and Product Development (78%).
Main locations of FRATCH Experts, who have recently used Dagster
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 Request a free demo
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Philipp Thomaschewski
FRATCH CEO
