Apache Superset Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Apache Superset
Dmitry Pankov
Last position:
Freelance Digital Marketing Analyst at Freelance
- Marketing Strategy: Lead the end-to-end analysis and evaluation of cross-channel marketing campaigns across the entire Customer Journey. My focus is identifying optimization potential and deriving clear, actionable recommendations that drive measurable business impact.
- Data Science & AI: Advanced predictive modeling (Churn, LTV), market basket analysis, clustering, and real-time AI-powered audience discovery utilizing RAG/LLMs.
- Marketing Analytics & Measurement: End-to-end attribution analysis, Marketing Mix Modeling (MMM), audience segmentation, conversion path analysis, and A/B testing across all major platforms.
- Data Engineering & Reporting: Designing and managing robust, multi-platform data pipelines (BigQuery, GCP) for data consolidation, automated dashboard generation, and critical API integrations.
Nitin Bhardwaj
Last position:
Financial Analytics Lead at Independent Consultant
Led FP&A tech transformation for a 9-figure business – from resolving legacy technical debt to leading AI-native EPM implementation
- Driving end-to-end FP&A transformation, from architecture redesign through EPM tool selection to rollout
- Ran evaluation of 12+ EPM platforms, from vendor negotiation to selection framework tied to long-term planning
- Diagnosed constraints in financial planning architecture, presented findings to the CFO, and secured executive mandate to redesign FP&A infrastructure from the ground up
Ashwin Parthasarathy
Last position:
Data Scientist at Mercor Intelligence
- Elevated LLM output reliability by engineering domain-specific prompts and evaluation logic, improving reasoning consistency across production language model workflows.
- Designed advanced coding benchmarks and validated solutions to strengthen training and evaluation datasets, improving model performance on technical problem-solving tasks.
- Designed and implemented automated evaluation frameworks for technical reasoning tasks; optimized LLM output reliability by 15% through rigorous prompt engineering and rubric-based benchmarking.
Bidya Bibhu
Last position:
Global Lead (Product) – Payments Platform (Risk & Data Products) at Chargebee
- Own strategy and roadmap for the payment orchestration and risk intelligence products serving enterprise subscription customers.
- Conduct deep workflow discovery and user interviews to redesign onboarding experience, resulting in 5× funnel throughput and 70% reduction in manual steps.
- Define PRDs for scalable data pipelines, fraud signals, and automation logic, improving insight accuracy and speed of decision-making by 30%.
- Partner with engineering, data, design, and security to ship 15+ enterprise features with 100% successful release quality.
- Introduce risk analytics dashboards and performance KPIs, reducing investigation time by 40% and improving visibility across teams.
- Lead prioritization of new capabilities, tech debt, and security initiatives (PCI DSS, access controls, auditability).
- Lead development of ML-based fraud detection models (regression, decision trees) to identify high-risk transactions, reducing chargebacks by 20%.
- Design end-to-end analytics dashboards (Tableau, Redshift) to visualise global risk exposure, cutting onboarding SLA from 2.4 days to 3 minutes.
- Partner with engineering and data teams to deploy scalable payment risk frameworks, enhancing compliance visibility and decision speed.
- Mentor analysts and data scientists through agile sprint cycles, embedding a data-driven culture across risk operations.
Anna Moiseeva
Last position:
Senior Product Manager / Project Lead at Keepcode
- Define and own the end-to-end product lifecycle for flagship project (web and mobile app, more than 15M total users). Lead delivery, track and analyse product metrics, data and KPIs.
- Drive roadmap planning: translate long-term product vision and product strategy, business objectives, customer needs into a prioritised backlog for cross-functional teams.
- Lead cross-functional teams using Agile methodologies: facilitate daily stand-up meetings, sprint planning, poker-planning, retrospectives, and demos; conduct feature kick-off meetings.
- Maintain clear and trust communication with stakeholders through product updates, presentations and regular reports, acting as a bridge between technical teams and business leaders.
- Apply data-driven hands-on approach with product analytics, experimentation, and customer interviews for decision-making across product development.
- Contribute to annual strategic planning with the Board by translating market dynamics, customer needs and competitive positioning into actionable product roadmap and goals.
- Increased in-app conversion by 13% and reduced average purchase time by 50% through Google Analytics implementation and UX/UI redesign.
- Reduced support workload by 60% by implementing an AI-powered support system with a knowledge base and automated Jira ticket creation.
- Reduced key client churn by 14% and increased internal project revenue by 22% YoY by building a data-driven analytics framework in Power BI.
- Optimised referral program costs by 17% and improved retention by 22%.
- Enabled global market expansion across 6+ regions by leading the design and delivery of a payment integration platform, integrating 10+ payment methods via a unified API.
Jürgen Fey
Last position:
AR/VR/XR Architect at Deutsche Telekom
- Defined a generic, universal system platform for XR (AR, VR) use cases as part of the European IPCEI initiative
- Evaluated available options for backend, streaming and client implementations
- Developed PoC implementations and conducted technical evaluation of potential components
- Led the development team and defined the target system architecture for a complete end-to-end solution
- Designed and integrated GenAI PoCs (LLM, time series, prediction, agents)
- Researched NeRFs and Gaussian Splatting for XR content creation and analyzed methods to measure and reduce system latencies
- Worked on GPU scheduling, CUDA and related topics
- Defined and implemented backoffice and end user dashboards using Grafana and Superset
- Used Jaeger tracing to analyze specific events
- Defined a GenAI-based automatic alerting feature for specific events and event clusters with KPI values
- Integrated AR/VR/XR IoT devices
- Collaborated with business development and UX teams in a parallel design thinking track to align technical and customer journey scopes
- Integrated the new services and platform into existing telco environments and analyzed tools like Camara for partial integration and handover scenarios
Marco Poloni
Last position:
Senior Siebel CRM and BI Architect
- Maintenance and enhancement of a Siebel CRM Service & Marketing implementation (Siebel 23.1, OpenText, OBIEE, Informatica).
Gyan Prakash
Last position:
Senior DevOps and Cloud Architect at Bosch
- Architected and operated cloud-based data and ML platforms for autonomous driving and parking systems, supporting large-scale (multi PB scale) simulation and vehicle data ingestion.
- Implemented security, compliance, and governance standards across Azure subscriptions and cloud resources.
- Managed GitHub organizations and CI/CD pipelines to improve deployment reliability and developer productivity.
- Contributed to hiring and technical interviews as part of the recruitment panel.
Fabian Crabus
Last position:
Short project: Converting monocular images
- Converting monocular images into depth maps and point clouds as training data for Jetson and Zed stereo cameras
- Developing a drone detection system based on audio and video using Python
Rüdiger Kohl
Last position:
Data Analyst and Reporting Manager at Energieversroger
Implemented Power BI reporting for various departments at an energy provider. Agile project: responsible for organizing and coordinating with departments and IT, and regularly presented interim steps and results to project management.
Jörg-Ulrich Hammerbacher
Last position:
Data flows for health insurance providers
- Further development and creation of data flows for health insurance providers
- Data management across various storage systems (DB2, MSSQL, PostgreSQL, S3, custom APIs, ...)
- Documentation and training
- Planning and deployment of NiFi 2.x (major upgrade)
- Integrating Grafana for visualization, monitoring, and alerting
- Extensive use of the NiFi API to continuously monitor the system and its components
Philipp Kunz
Last position:
Crisis Infrastructure
- Working on a project related to redundant crisis infrastructure
- Used tech: TypeScript, Node, Java, MongoDB, Kafka
Discover over 15,000 top freelancers
Statistics of experts using Apache Superset
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
2 years
Positions per freelancer
13
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Information Technology, Banking and Finance, Transportation
Certification focus areas
Information Technology, Business Intelligence, Marketing
Bachelor's degree or higher
89%
Master's degree or higher
67%
Doctorate
22%
Certifications per freelancer
2
Most common languages
German, English, Hindi
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 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 Apache Superset
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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Superset dashboards
Apache Superset is an open-source business intelligence tool for interactive dashboards and self-service analytics. Teams use it to explore data, track KPIs, and share clear views of operational, product, and finance data. Strong specialists know how to turn raw warehouse data into usable reporting layers.
Typical work
- Build charts, filters, and dashboard layouts for different audiences
- Connect Superset to warehouses, databases, and semantic layers
- Tune SQL queries for faster, cleaner reports
- Set up roles, row-level access, and shared views
Ecosystem fit
Superset often sits on top of PostgreSQL, MySQL, Snowflake, BigQuery, Trino, and other SQL sources. It also relies on a solid Python, SQL, and cloud setup around authentication, caching, and deployment. Experienced professionals know how to keep the BI layer aligned with the data model beneath it.
When companies bring help
Companies usually look for freelance Superset expertise when reporting is growing faster than the internal team can support it. That is common in product analytics, finance reporting, operations, and internal dashboards. In Germany, this often means working with teams that expect clear documentation, precise handover, and smooth remote collaboration.
What strong specialists do
A strong specialist does more than arrange charts. They structure filters well, keep dashboards readable, and understand where the SQL should live. They also care about permissions, naming, and maintainability so business users can trust the numbers they see.
Common project signals
- Dashboards are slow, inconsistent, or hard to use
- Multiple teams need different views of the same data
- Access control and sharing are not set up cleanly
- The company needs help from a Superset, Apache Superset, or BI specialist
- Existing reports need redesign, cleanup, or migration
Frequently asked questions
Need clarity? These are the questions we hear most often about Apache Superset.
Apache Superset is used to build dashboards, charts, and ad hoc analytics on top of SQL data sources. Companies use it for operational reporting, product tracking, finance views, and internal self-service analytics. It is a good fit when teams want flexible exploration without building a custom reporting app from scratch.
Superset is often chosen when a company prefers an open-source BI layer and strong SQL-driven control. Tableau and Power BI can be better known in business teams, but Superset is attractive when the stack is more technical and the data lives in warehouses or databases already. The right choice depends on governance, deployment needs, and who will maintain the reports.
A strong Apache Superset specialist should know SQL well, understand dashboard design, and be comfortable with common data platforms. Python knowledge helps with deployment, configuration, and extensions. Experience with authentication, permissions, and caching is also useful when the setup needs to be secure and stable.
Simple dashboard work may only need a specialist who understands chart setup, filters, and SQL queries. More complex projects need deeper experience with data modeling, row-level security, performance tuning, and deployment. If the work touches shared metrics or sensitive data, choose someone who has handled production Superset setups before.
Apache Superset freelance help makes sense when reporting work is urgent, the internal team lacks BI bandwidth, or the current dashboards need a cleanup. It is also useful during migrations from another BI tool or when new data sources must be added quickly. Freelancers can step in for design, build, review, or handover work.
Yes. Superset projects are often well suited to remote work because most of the effort happens in SQL, dashboard design, and configuration. On-site work can still help during stakeholder workshops or access reviews, but many teams in Germany collaborate effectively with specialists who work remotely and document clearly.
Look for clear dashboard structure, clean SQL, and careful handling of access rules in Apache Superset. Good specialists ask about the data source, the business question, and who will use the dashboard. They should also explain trade-offs in performance, filter design, and maintainability in plain language.
A solid Superset professional will ask where the data comes from, which KPIs matter, and who needs access. They should also confirm deployment details, existing data models, and whether the work is a build, migration, or cleanup. These answers help shape the dashboard design and avoid rework later.
The average hourly rate of freelancers in Germany who have used Apache Superset in their recent projects is 83 €, which corresponds to a daily rate of about 666 € based on an 8-hour working day.
Of the freelancers in Germany who have used Apache Superset in their recent projects, 89% hold at least a Bachelor's degree, 67% hold at least a Master's degree, and 22% hold a doctorate.
On average, freelancers in Germany who have used Apache Superset in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Germany who have used Apache Superset in their recent projects are German (100%), English (100%), and Hindi (25%).
The most common industries among freelancers in Germany who have used Apache Superset in their recent projects are Information Technology (92%), Banking and Finance (67%), and Transportation (42%).
The most common business areas among freelancers in Germany who have used Apache Superset in their recent projects are Business Intelligence (100%), Information Technology (92%), and Product Development (83%).
Main locations of FRATCH Experts, who have recently used Apache Superset
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 
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Philipp Thomaschewski
FRATCH CEO
