Apache Superset Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used Apache Superset
Dmitry P.
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 B.
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 P.
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 B.
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 M.
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.
Gyan P.
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.
Discover over 15,000 top freelancers
Statistics of experts using Apache Superset
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
3 years
Positions per freelancer
6
Top business areas
Business Intelligence, Product Development, Information Technology
Top industries
Information Technology, Banking and Finance, Retail
Certification focus areas
Information Technology, Business Intelligence, Marketing
Bachelor's degree or higher
100%
Master's degree or higher
80%
Doctorate
20%
Certifications per freelancer
3
Most common languages
German, English, Hindi
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 Berlin 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 Berlin 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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Apache Superset 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 (83%)
- Banking and Finance (50%)
- Retail (50%)
- Professional Services (33%)
- Advertising (17%)
- Automotive (17%)
- Cosmetics (17%)
- Education (17%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Dashboards that matter
Apache Superset is an open-source business intelligence and data visualization tool for SQL-first analytics. It helps teams turn warehouse data into dashboards, charts, and explored views that business users can read without writing code. Strong experts focus on clarity, fast filtering, and reliable metric definitions.
Where it fits
Superset is common in data teams that need shared reporting across product, operations, finance, or marketing. It works well when analysts want direct access to trusted data without building a custom front end.
- Interactive dashboards and drilldowns
- Chart design for SQL-backed reporting
- Row-level access and permission setup
- Connections to warehouses and databases
Ecosystem and setup
The Apache Superset stack usually includes SQLAlchemy data sources, metadata databases, caching, and a web server layer. Professionals who know its ecosystem also understand auth, alerting, and deployment patterns on containers or cloud hosts. They keep the app responsive and the charts consistent.
When to bring in help
Companies hire freelance Superset specialists when a dashboard project is stalled, the current setup is slow, or reporting has become hard to trust. That is common during a warehouse rollout, a BI migration, or a cleanup of many ad hoc charts. In Berlin, this often comes up in product teams and data-heavy service firms that need clear collaboration across English-speaking and German-speaking teams.
What strong specialists do
A good Apache Superset professional does more than build charts. They shape the data layer, define reusable datasets, reduce duplicated logic, and make sure permissions match how the business works.
- SQL and semantic layer thinking
- Dashboard structure and UX clarity
- Performance tuning for queries and caching
- Secure access by role or team
Quality signs
Strong specialists write clean SQL, understand the warehouse behind the dashboard, and can explain tradeoffs in simple terms. They test filters, time ranges, and permissions instead of only checking that a chart renders. They also document dataset logic so other experts can maintain the setup later.
Frequently asked questions
Curious about Apache Superset? Here are the answers that come up again and again.
Apache Superset is used for dashboards, chart exploration, and SQL-based reporting on top of warehouse or database data. Companies use it when they want self-service analytics without building a custom BI application from scratch. It is especially useful when many teams need the same trusted metrics.
Apache Superset is open source and fits teams that are comfortable working close to SQL and their own data stack. Tableau and Power BI often appeal to broader business audiences and include more packaged features, while Superset gives more control over infrastructure and data access patterns. The right choice depends on governance, hosting, and how technical the users are.
A strong Superset specialist usually brings solid SQL, data modeling, and warehouse knowledge. Experience with authentication, permissions, caching, and container deployment helps a lot. Skills in dashboard UX also matter, because clarity is often more important than visual polish.
A small Apache Superset setup may only need someone who can connect data sources, build a few dashboards, and set permissions correctly. More complex work needs deeper knowledge of metadata databases, performance tuning, and governance. If the reporting layer is business-critical, you want someone who has worked through failures, not just first-time installs.
Yes, Apache Superset work is often well suited to remote collaboration because most tasks happen in the data stack, not on a physical site. Berlin teams often mix on-site workshops with remote implementation, especially when stakeholders speak different languages or sit in different offices. Clear access to the warehouse and dashboard requirements matters more than location.
If Apache Superset dashboards are slow, confusing, or built from inconsistent metrics, it is time to bring in a specialist. Other signs are duplicated charts, broken filters, weak permissions, or a setup that only one person understands. These problems usually grow once many teams depend on the same reports.
Look for someone who can explain how Apache Superset is wired from data source to dashboard, not just how to click through the interface. Good answers should cover SQL, dataset design, permissions, and performance. A strong portfolio shows real reporting problems solved, such as clearer metrics, faster load times, or cleaner governance.
Before starting with Superset, a freelancer should understand the data source, the user groups, and the key business questions each dashboard must answer. Access to sample queries, metric definitions, and permission rules saves time and prevents rework. The best projects start with a clear view of who will use the reports and how often.
The average hourly rate of freelancers in Berlin, Germany who have used Apache Superset in their recent projects is 66 €, which corresponds to a daily rate of about 525 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Apache Superset in their recent projects, 100% hold at least a Bachelor's degree, 80% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Apache Superset in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 3 years.
The most common languages among freelancers in Berlin, Germany who have used Apache Superset in their recent projects are German (100%), English (100%), and Hindi (50%).
The most common industries among freelancers in Berlin, Germany who have used Apache Superset in their recent projects are Information Technology (83%), Banking and Finance (50%), and Retail (50%).
The most common business areas among freelancers in Berlin, Germany who have used Apache Superset in their recent projects are Business Intelligence (100%), Product Development (100%), and Information Technology (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.
Countries:
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
