Big Data Experts in Switzerland
matched in minutes from over 15,000 CVs with the power of AIHire experts who design data platforms, process streaming events with Apache Kafka and Spark, and turn complex datasets into usable analytics. FRATCH matches you quickly and precisely with vetted, available freelancers who fit your technical needs.
Meet FRATCH Experts in Switzerland, who have recently used Big Data
Peter P.
Last position:
Consultant Strategy & Digital at Polynova AG
- Designed the growth and digital strategy for an EU distributor
- Established a differentiation strategy focusing on horizontal portfolio expansion, ESG and SG&A cost reduction using new digital sales and service channels
- Offered same-day logistic delivery for key customers, increasing customer satisfaction by 25%
Michael R.
Last position:
Project Manager, Hustler, & Hound "Project Metrics on the Internet" at Global-KPI
- Project management, hustler (identifying the full financial picture including sales, pitching, financial analysis, & business models) and hound (identifying real customer needs including research & analysis) to build a project metrics system on the Internet.
- The system allows users to make informed decisions to keep projects on track according to key data or to realign them when they deviate negatively from the plan.
- Providing a forecast alongside the current status.
- Automatic calculation and display of metrics based on user-entered project key data.
- Skills: project management, project metrics.
- Problem space: Co-Innovation Builder (CIB), corporate innovation team setup, team profile / team canvas, design thinking, highlighting people, creating customer personas, market exploration, defining the problem.
- Solution space: narrowing the problem, how to turn ideas into solutions, how to turn solutions into a concept, customer journey, business model canvas, value proposition canvas, prototyping, refining the prototype and business model canvas, final pitch.
Ursula M.
Last position:
Business Mentor at RoleModel Rebels
- Mentor female students and professionals in advancing their careers, particularly as aspiring tech entrepreneurs.
Wolf P.
Last position:
Interim Head of IT POS (GK & SAP) at Depot GDC
- Interim head of IT POS on GK and SAP basis for DEPOT GDC.
- Responsible for the stability and further development of the POS base platform.
- Managing adjustments and rollouts in the POS environment in coordination with the SAP backend.
Deschances T.
Last position:
Senior Data Architect at Odd Parrot
- Supporting UBS Wealth Management as Senior Data Architect within a multi-year enterprise data mesh transformation.
- Guiding a strategic stream to design and implement a data product aligned with UBS’s enterprise-wide data mesh framework.
- Leading architecture definition, governance alignment, and cross-domain integration to ensure scalable, compliant, and reusable data products across global stakeholders.
- UBS – Global Wealth Management Data Product Architecture: Designed an enterprise-aligned data product spanning 26 countries, defining data contracts, metadata standards, and federated architecture patterns enabling cross-domain reuse across the bank.
- Defined cross-jurisdictional data policies and access controls to ensure compliance with regulatory requirements across multiple regions (EU, APAC, LATAM, CH).
Kalin S.
Last position:
Sr Data Engineer / Architect at Samsung Logistics
- Architected and implemented a structured three-layer enterprise data warehouse model in Azure, establishing a robust and scalable data environment.
- Migrated legacy stored procedures to streamlined Azure Data Factory (ADF) pipelines, enhancing data processing efficiency.
- Introduced comprehensive Git-based source control, ensuring rigorous version management and collaborative development practices.
- Established automated data quality frameworks with proactive monitoring and alerting, significantly improving data integrity and reliability.
- Spearheaded the design of an enterprise data model, enabling a self-service BI environment that empowered business teams with advanced analytics capabilities.
- Developed and delivered insightful dashboards and reports in Power BI, transforming raw data into actionable business insights.
- Technologies: Azure Data Factory (ADF), Azure, MS-SQL, DataVault 2.0, Git, Power BI, data quality automation, Agile/Scrum, data modeling, self-service BI, stakeholder management.
Ned O.
Last position:
Tech Lead at R3leaf GmbH
- Autonomously drafted architecture and delivered production code at startup pace
- Wrangled diverse geospatial formats (NETCDF, GeoTIFF, GML) into unified standards and built scalable climate data visualisations from hundreds of GBs of geodata in a production web app
- Mentored developers, facilitated AI skill sharing, and contributed to competitive strategy with C-Level leadership
Hrvoje K.
Last position:
Researcher, Investment Research Institute at Pictet Group
- Designed and implemented AI-enhanced macro-factor models improving asset allocation decisions across multi-asset portfolios
- Developed machine learning-based FX trading strategies, integrating macroeconomic and sentiment data to identify profit opportunities
- Models adopted by the investment committee to guide equity and currency exposure
- Collaborated with portfolio managers to translate analytical output into actionable investment insights
Karl E.
Last position:
incl. CI/CD, automation at AALS Software AG
- Designed and delivered a practical real-time course on Flink and Hadoop with MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, and Kafka
- Gained extensive DevOps and CI/CD experience
- Created ETL/ELT pipelines with Apache tools and Pentaho
- Led projects in municipal software, financial services, and big data with Kafka
- Developed AI/NLP models and chatbots with RASA, Chatter, and Dialogflow
- Built and managed a TypeDB knowledge database
- Worked with OpenStack, Kubernetes, and Podman
Tom D.
Last position:
Leader of Start-up programs
Discover over 15,000 top freelancers
Statistics of experts using Big Data
Aggregated from the professional profiles of matched freelancers.
Experience
22 years
Position duration
2.5 years
Positions per freelancer
15
Top business areas
Information Technology, Business Intelligence, Marketing
Top industries
Information Technology, Banking and Finance, Education
Certification focus areas
Information Technology, Project Management, Quality Assurance
Bachelor's degree or higher
100%
Master's degree or higher
75%
Doctorate
13%
Certifications per freelancer
3
Most common languages
English, German, French
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 Switzerland 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 Switzerland using Big Data
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.
Big Data 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 (90%)
- Banking and Finance (80%)
- Education (70%)
- Manufacturing (70%)
- Professional Services (70%)
- Energy (50%)
- Healthcare (40%)
- Insurance (40%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Big Data covers
Big Data describes methods and systems for collecting, storing, processing and analysing datasets that exceed the practical limits of conventional databases. It supports high-volume event streams, machine learning inputs, operational reporting and large-scale search. The work combines distributed storage, parallel computation, data quality and secure access.
Typical applications
Companies use Big Data to turn raw information into dependable products and decisions:
- Build data lakes and lakehouses for structured and unstructured information
- Process clickstreams, transactions, sensor events and application logs
- Prepare features and training data for machine learning systems
- Create customer analytics, forecasting and real-time monitoring
Core ecosystem
The ecosystem commonly includes Apache Hadoop, Apache Spark, Apache Kafka, Flink, Hive and Trino, together with cloud services such as Amazon S3, Google Cloud Storage and Azure Data Lake Storage. Strong work also involves SQL, Python or Scala, distributed databases, orchestration with Apache Airflow, and formats such as Parquet. Containerisation and infrastructure automation help teams operate these components reliably.
When specialists help
Freelance expertise is useful when a company is moving from a legacy warehouse, consolidating scattered data sources or introducing streaming analytics. It can also support a new lakehouse, a migration to cloud infrastructure or a platform that must serve both reporting and machine learning. In Switzerland, collaboration may combine remote delivery with on-site workshops and clear communication across technical and business teams.
Delivery responsibilities
A specialist can define ingestion patterns, model data domains, select storage formats and build batch or streaming pipelines. The work may include schema evolution, data lineage, access controls, observability, cost management and recovery procedures. Good delivery leaves behind tested workflows, useful documentation and clear ownership rather than an opaque collection of tools.
What strong experts show
Strong Big Data professionals understand trade-offs instead of selecting technology by fashion. They can explain latency, consistency, partitioning, retention and failure handling in terms of business needs. Look for evidence of production data platforms, careful validation, secure design and effective cooperation with analytics, software and operations teams. They should also make complex distributed behaviour understandable to non-specialists.
Frequently asked questions
Need clarity? These are the questions we hear most often about Big Data.
Big Data is used to collect and analyse information from sources such as transactions, devices, applications and customer interactions. Companies use it for real-time monitoring, forecasting, search, recommendation systems, reporting and machine learning.
Big Data platforms are designed for distributed processing and often handle varied or rapidly arriving information at broad scale. A traditional warehouse remains well suited to governed, structured reporting, while modern lakehouse designs combine warehouse-style analysis with flexible data storage.
A strong Big Data specialist often brings SQL, Python or Scala, cloud storage, data modelling and orchestration experience. Knowledge of Apache Kafka, Apache Spark, Apache Airflow, machine learning workflows, security and infrastructure automation is also valuable.
The right level depends on the project’s risk and scope. A contained pipeline may need a specialist who can work within an established platform, while a migration or streaming system needs production experience with architecture, failure recovery, governance and operational ownership.
Big Data work is often suitable for remote collaboration because platforms, repositories and cloud environments can be accessed securely online. On-site sessions in Switzerland can still help with discovery, data ownership decisions and workshops involving several business teams.
The choice depends on workload shape, latency needs, existing infrastructure and team skills. Apache Spark is widely used for batch and combined workloads, while Apache Flink can suit stateful stream processing; a careful assessment should come before committing to either.
Review how the specialist handles data validation, schema changes, lineage, access control, monitoring and failed jobs. High-quality Big Data delivery is reproducible, testable and documented, with performance and operating costs explained in terms the business can use.
Ask about source systems, data ownership, privacy requirements, service-level expectations and the current platform. A Big Data assignment is easier to deliver when the specialist also understands who consumes the data, how success is measured and which internal teams will maintain the result.
The average hourly rate of freelancers in Switzerland who have used Big Data in their recent projects is 134 €, which corresponds to a daily rate of about 1,071 € based on an 8-hour working day.
Of the freelancers in Switzerland who have used Big Data in their recent projects, 100% hold at least a Bachelor's degree, 75% hold at least a Master's degree, and 13% hold a doctorate.
On average, freelancers in Switzerland who have used Big Data in their recent projects have 22 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers in Switzerland who have used Big Data in their recent projects are English (100%), German (80%), and French (40%).
The most common industries among freelancers in Switzerland who have used Big Data in their recent projects are Information Technology (90%), Banking and Finance (80%), and Education (70%).
The most common business areas among freelancers in Switzerland who have used Big Data in their recent projects are Information Technology (90%), Business Intelligence (80%), and Marketing (80%).
Main locations of FRATCH Experts, who have recently used Big Data
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:
Zurich Geneva Basel Bern Request a free demo
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Philipp Thomaschewski
FRATCH CEO
