Apache Spark Experts in Switzerland
to process data at scale with fast, precise AI matchingHire experts who build distributed data pipelines, streaming applications and machine learning workflows with Apache Spark, using tools such as PySpark, Scala and Delta Lake. FRATCH matches you quickly with vetted, available freelancers who fit your technical needs.
Meet FRATCH Experts in Switzerland, who have recently used Apache Spark
Marco S.
Last position:
MS Power BI Developer at Bayern Facility Management GmbH
Power BI development and administration, training, consulting, data modeling, DAX development
MS Fabric, MS Power BI, DAX
Paulo M.
Last position:
Power BI & Microsoft Fabric BI Specialist at Financial Bank
- Developed Power BI semantic models and DAX measures, reducing financial reporting time.
- Implemented RLS, strengthening role-based data access and governance controls.
- Delivered automated KPI dashboards, accelerating reporting and decision-making.
- Designed a Fabric Lakehouse with Bronze, Silver and Gold layers, improving data processing.
- Built Fabric pipelines and PySpark transformations, reducing processing time by 40%.
- Built and optimized Power BI semantic models, improving report performance, scalability and usability.
- Optimized UNIX universes, reducing query execution time by 40%.
Environment: Power BI (Semantic Models, DAX, RLS), Microsoft Fabric (Lakehouse, Pipelines, PySpark), SAP BO 4.3/2025, Web Intelligence, IDT/UNX, InfoBurst, 360Suite, GitHub, JIRA.
Georgios S.
Last position:
Senior Software Engineer at UBS Bank
- Implementations of a code refactoring framework able to refactor thousands of repositories leveraging Generative AI
- Use Python (Django, Flask, FastAPI), Java and Typescript in Azure Cloud (Data Lake, VMs, AI) and GitLab infrastructure
- Mentoring and pair programming
- Obtained Azure AI-900 and AI-102 certifications
Ursula M.
Last position:
Business Mentor at RoleModel Rebels
- Mentor female students and professionals in advancing their careers, particularly as aspiring tech entrepreneurs.
Matthias I.
Last position:
Fractional CTO (Principal Engineer / Technical Architect)
- Designed large-scale systems and APIs serving thousands of concurrent users.
- Refactored a 650k-LOC monolith and led full AWS migration for stable performance.
- Introduced SLO-based observability, improving reliability and recovery flow.
- Optimised cloud and databases, achieving significant cost and latency reduction.
- Delivered LLM, RAG, and document-automation pipelines adopted in production.
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.
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
Discover over 15,000 top freelancers
Statistics of experts using Apache Spark
Aggregated from the professional profiles of matched freelancers.
Experience
24 years
Position duration
2.6 years
Positions per freelancer
15
Top business areas
Information Technology, Project Management, Business Intelligence
Top industries
Banking and Finance, Information Technology, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
75%
Doctorate
13%
Certifications per freelancer
4
Most common languages
English, German, French
Speak two or more languages
100%
Based on our profile pool as of 16 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Switzerland 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 Switzerland using Apache Spark
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 16 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Apache Spark experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Banking and Finance (100%)
- Information Technology (100%)
- Professional Services (75%)
- Education (63%)
- Energy (63%)
- Manufacturing (63%)
- Healthcare (50%)
- Government and Administration (50%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Distributed data processing
Apache Spark is an open-source engine for processing large datasets across clusters. It supports batch workloads, interactive analysis, real-time streams and machine learning in one ecosystem. Teams use it to turn raw events, files and database records into reliable data products.
Core workloads
- Build batch pipelines for data lakes and warehouses
- Process real-time events with Structured Streaming
- Prepare features and train models with MLlib
- Run exploratory analysis with Spark SQL and DataFrames
- Reconcile and enrich data from multiple systems
Spark can read from object storage, relational databases, Kafka and distributed file systems. Its APIs for Python, Scala, Java and SQL let teams choose the right balance of speed, control and accessibility.
Ecosystem and tooling
Apache Spark is commonly used with PySpark, Scala, Spark SQL, Delta Lake and MLlib. Strong specialists also understand Kafka, Databricks, Kubernetes, Hadoop-compatible storage, Airflow and cloud data services. They tune partitioning, caching, joins and file formats such as Parquet to improve reliability and cost control.
When expertise matters
Companies often bring in freelance expertise when a prototype must become a production pipeline, when streaming workloads become difficult to operate or when slow jobs affect reporting and analytics. In Switzerland, projects may span finance, pharmaceuticals, manufacturing, retail and public services. Clear documentation and collaboration across German-, French- or English-speaking teams can be important.
- Jobs miss processing windows or fail under changing data volumes
- Cluster costs rise without clear workload visibility
- Batch and streaming logic produce inconsistent results
- Legacy Hadoop workloads need a modern migration path
Delivery skills
A capable professional starts with data contracts, workload patterns and operational requirements rather than simply adding cluster capacity. They design for fault tolerance, idempotent processing, schema evolution and observable pipelines. They also test transformations, manage dependencies and explain trade-offs to data, product and infrastructure teams.
Choosing a specialist
Review work that shows production ownership, not only notebook experiments. Ask how the specialist diagnosed slow stages, handled skewed joins, secured sensitive data and monitored failed jobs. Practical knowledge of deployment, access control, testing and incident response matters as much as Spark API knowledge. Remote work is often effective when repositories, environments and responsibilities are clearly organized, while on-site sessions can help with complex stakeholder workshops.
Frequently asked questions
Questions about Apache Spark? Start with the answers below.
Apache Spark is used to process and analyze large datasets across distributed computing resources. Companies use it for batch ETL, data lake preparation, streaming event processing, SQL analytics and machine learning feature pipelines.
Apache Spark provides a general processing engine with APIs for SQL, streaming and machine learning, while Hadoop is a broader ecosystem that traditionally centers on distributed storage and MapReduce processing. Spark can work with Hadoop-compatible storage and is often chosen for more interactive or iterative workloads.
Apache Spark is useful when workloads need flexible transformations, custom code, distributed file processing or integration with streaming systems. A cloud data warehouse may be simpler for governed relational analytics, so the decision depends on data shape, latency, operations and existing architecture.
A strong Apache Spark specialist often works with Python or Scala, SQL, Kafka, cloud storage and orchestration tools such as Airflow. Knowledge of Delta Lake, Kubernetes, data modeling, security and observability helps turn Spark jobs into maintainable production services.
The right level depends on the workload, not a fixed number of years. A simple batch transformation may need focused Spark skills, while streaming, regulated data, complex joins or cluster operations call for a professional who has handled comparable production risks.
Apache Spark projects are often suitable for remote collaboration because code, pipelines and infrastructure can be reviewed in shared environments. On-site work may still help during architecture workshops, data-access coordination or stakeholder sessions, especially when teams have specific German, French or English language needs.
Ask the specialist to explain partitioning, shuffle behavior, skew handling, schema changes and failure recovery in a relevant scenario. Review tests, monitoring, deployment practices and documentation, not only whether a notebook produces the expected result.
Apache Spark work extends beyond writing transformations. Professionals are expected to understand distributed execution, resource management, data quality, security and the operational impact of design choices across batch and streaming pipelines.
The average hourly rate of freelancers in Switzerland who have used Apache Spark in their recent projects is 101 €, which corresponds to a daily rate of about 810 € based on an 8-hour working day.
Of the freelancers in Switzerland who have used Apache Spark 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 Apache Spark in their recent projects have 24 years of professional experience, with a single engagement typically lasting around 2.6 years.
The most common languages among freelancers in Switzerland who have used Apache Spark in their recent projects are English (100%), German (75%), and French (63%).
The most common industries among freelancers in Switzerland who have used Apache Spark in their recent projects are Banking and Finance (100%), Information Technology (100%), and Professional Services (75%).
The most common business areas among freelancers in Switzerland who have used Apache Spark in their recent projects are Information Technology (100%), Project Management (75%), and Business Intelligence (63%).
Main locations of FRATCH Experts, who have recently used Apache Spark
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.
Zurich Request a free demo
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Philipp Thomaschewski
FRATCH CEO
