Dagster Experts in Germany
matched quickly through AI and vetted freelance expertiseHire experts who design observable data assets, orchestrate Python pipelines and connect Dagster with dbt, Spark, cloud storage and modern data platforms. Get precisely matched with vetted, available freelancers who can contribute remotely or on site in Germany.
Meet FRATCH Experts in Germany, who have recently used Dagster
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
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.
Stephan B.
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
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
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
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
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
14 years
Position duration
1.5 years
Positions per freelancer
11
Top business areas
Information Technology, Product Development, Business Intelligence
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
50%
Doctorate
33%
Certifications per freelancer
5
Most common languages
German, English, Spanish
Speak two or more languages
100%
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 Germany 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 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 19 Sep 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 (50%)
- Professional Services (50%)
- Education (38%)
- Energy (38%)
- Insurance (38%)
- Transportation (38%)
- Manufacturing (38%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Dagster does
Dagster is a Python-based orchestration framework for building, scheduling and monitoring data workflows. Its asset-oriented model represents tables, files, models and other outputs as first-class parts of a data platform. Teams use it to make dependencies, data quality and operational status easier to understand than in a collection of isolated scripts.
Core capabilities
Dagster coordinates pipelines across ingestion, transformation, validation and delivery. Software-defined assets describe how data is produced, while jobs, schedules and sensors control when work runs. Partitions support incremental processing, and resources keep connections to warehouses, APIs, storage and compute services configurable across environments.
- Model upstream and downstream data dependencies
- Schedule recurring asset materializations
- Trigger work from files, events or external systems
- Add checks for freshness, schema and data quality
Ecosystem and tooling
Dagster integrates with common data tooling through resources, I/O managers and reusable integrations. Specialists often connect it with dbt, Spark, DuckDB, Snowflake, BigQuery, Kubernetes, object storage and cloud execution services. Python packaging, environment management, CI pipelines and observability practices are also central to maintainable Dagster repositories.
Where companies use it
Companies bring Dagster into analytics platforms, lakehouses, machine learning data preparation and operational reporting. It can coordinate batch ingestion, transformation and publishing across teams without hiding the logic inside an opaque scheduler. In Germany, it can support distributed delivery with remote collaboration, while clear documentation helps teams working across locations and languages.
- Replace fragile cron and script-based workflows
- Coordinate dbt models with upstream ingestion
- Prepare reproducible datasets for machine learning
- Operate multi-environment data pipelines
When freelance expertise helps
Freelance specialists are useful when a team is introducing Dagster, migrating from Airflow or another scheduler, or bringing order to a growing data estate. They can define asset boundaries, establish deployment patterns, connect existing tools and improve failure handling without disrupting critical data products. External support also helps when internal teams need focused delivery during a platform transition.
What strong experts deliver
Strong professionals understand both orchestration and the data systems around it. They design clear asset graphs, separate business logic from infrastructure, use partitions and backfills safely, and make runs easy to inspect and retry. They also test sensors and schedules, manage secrets, document ownership and align deployment with the company’s cloud or Kubernetes setup. Quality is visible in predictable operations, useful alerts and pipelines that remain understandable as requirements change.
Frequently asked questions
What clients ask us most about Dagster — answered in short.
Dagster is used to define, schedule and observe data workflows built from assets such as warehouse tables, files and machine learning datasets. Companies use it for ingestion, transformation, validation, backfills and publishing across cloud and on-premises systems.
Dagster emphasizes software-defined assets, typed configuration, lineage and data-aware observability, while Airflow is widely known for task-based directed acyclic graphs and scheduling. The better choice depends on the team’s existing ecosystem, operating model and need for asset-level visibility.
A strong Dagster specialist usually understands Python, SQL, data modeling and cloud storage as well as orchestration. Experience with dbt, Spark, Kubernetes, Snowflake, BigQuery, CI pipelines and data quality checks can be important for an end-to-end delivery.
The right Dagster expert should have delivered workflows with similar sources, destinations and operational constraints, not merely completed tutorials. Ask for examples involving partitions, retries, sensors, deployments and production monitoring that match the scope of your data platform.
Dagster is well suited to remote collaboration because its repositories, asset definitions and deployment configuration can be reviewed in version control. For teams in Germany, agree on documentation standards, working hours, language expectations and access procedures before implementation begins.
Dagster can be a strong fit when a company needs control over asset definitions, execution environments and integrations across several systems. A managed orchestrator may be preferable when minimizing operational ownership matters more than customizing the data platform.
A Dagster project may deliver an asset repository, schedules, sensors, partitioning logic, resources, tests and deployment configuration. It should also include run monitoring, failure handling, documentation and clear ownership for the data products it operates.
Look for a Dagster implementation with readable asset graphs, deterministic transformations, safe retries and tested backfills. Good work makes dependencies and failures visible, keeps credentials outside code, and provides practical runbooks for the people who operate the pipelines.
The average hourly rate of freelancers in Germany who have used Dagster in their recent projects is 102 €, which corresponds to a daily rate of about 812 € 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, 50% hold at least a Master's degree, and 33% hold a doctorate.
On average, freelancers in Germany who have used Dagster in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 1.5 years.
The most common languages among freelancers in Germany who have used Dagster in their recent projects are German (100%), English (100%), and Spanish (13%).
The most common industries among freelancers in Germany who have used Dagster in their recent projects are Information Technology (100%), Banking and Finance (50%), and Professional Services (50%).
The most common business areas among freelancers in Germany who have used Dagster in their recent projects are Information Technology (100%), Product Development (88%), and Business Intelligence (75%).
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
