Data Lake Experts in Switzerland
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Meet FRATCH Experts in Switzerland, who have recently used Data Lake
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
Rainer S.
Last position:
Data Engineer | Data Analyst | Power BI Architect
Banking project
- Analyzed and built a report store with the related data integration workflows.
- Developed and optimized T-SQL stored procedures and SSIS packages for data preparation and loading.
- Developed Power BI reports and delivered business reporting requirements.
Technologies: SQL Server, T-SQL, SSIS, Power BI, Visual Studio 2022, PowerShell, VBA, .NET
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).
Majk K.
Last position:
Program Manager Core, Aggregation, Edge Network 4.0 at Deutsche Glasfaser GmbH
- Roll-out of 12 core locations in Germany (covering core routers, route reflectors, border routers, CGNAT, Sedges)
- Roll-out of 40 aggregation locations in Germany (covering aggregation routers and BNG functionality)
- Roll-out of 230 edge locations in Germany (covering several edge routers per site)
- Network topology (re-) planning (core-to-core, core-to-agg, edge rings planning) including carrier management
- BNG (customer) migration to new network infrastructure including IT support
- Lawful interception and LIMS
- Network documentation with FNT Command, ServiceNow
- Network engineering handover to operations, process management
- Commercial and technological management, supplier management
- Management of architecture and design progress (HHLD, HLD, LLD)
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 Data Lake
Aggregated from the professional profiles of matched freelancers.
Experience
27 years
Position duration
2.3 years
Positions per freelancer
16
Top business areas
Information Technology, Business Intelligence, Project Management
Top industries
Banking and Finance, Information Technology, Manufacturing
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
83%
Doctorate
33%
Certifications per freelancer
4
Most common languages
English, German, French
Speak two or more languages
100%
Based on our profile pool as of 11 Nov 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 Data Lake
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 11 Nov 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Data Lake 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%)
- Manufacturing (67%)
- Automotive (50%)
- Education (50%)
- Energy (50%)
- Insurance (33%)
- Transportation (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What a Data Lake does
A Data Lake is a central storage environment for raw, semi-structured and structured data. It keeps information in its original form so teams can process it for analytics, machine learning, reporting and operational use cases. Unlike a traditional warehouse, it can support varied data types and evolving schemas.
Core architecture
A reliable lake separates storage, processing, governance and consumption. Specialists choose object storage such as Amazon S3, Azure Data Lake Storage or Google Cloud Storage, then shape access through catalogs, metadata and table formats such as Delta Lake, Apache Iceberg or Apache Hudi. Lakehouse designs add warehouse-style reliability and query performance.
- Define zones for raw, refined and curated data
- Select batch and streaming ingestion patterns
- Set retention, lineage and access policies
- Connect SQL, BI and machine learning workloads
Ecosystem and tooling
Data Lake work often spans Apache Spark, Apache Kafka, Flink, dbt and Airflow, alongside cloud-native orchestration and monitoring services. Strong professionals understand file formats such as Parquet, partitioning, schema evolution and workload cost control. They also connect catalogs and governance tools to the wider data platform.
When companies need specialists
Freelance expertise helps when a lake is expanding faster than its controls, when a migration must preserve data quality, or when teams need trusted pipelines for advanced analytics. Companies also bring in specialists to resolve slow queries, duplicated datasets, unclear ownership and insecure access. In Switzerland, collaboration may combine remote delivery with on-site workshops and clear communication across German, French, Italian or English-speaking teams.
- Replace fragile hand-built pipelines
- Modernize warehouse and lake architectures
- Prepare data for machine learning
- Establish governance and observability
Deliverables to expect
A project may produce an architecture decision record, ingestion pipelines, storage conventions, data quality checks and documented operating procedures. It can also include a catalog, lineage model, role-based permissions, infrastructure-as-code and dashboards for pipeline health. The right scope depends on data sources, compliance needs, query patterns and the teams responsible for ongoing ownership.
What strong professionals bring
Experienced specialists balance openness with control. They test schemas, handle late or duplicated records, design for recovery and explain trade-offs between object storage, warehouses and lakehouse tables. They measure success through trusted datasets, predictable operations and clear ownership rather than storage volume alone. They also leave behind documentation and patterns that internal teams can maintain.
Frequently asked questions
What clients ask us most about Data Lake — answered in short.
A Data Lake stores raw and processed information from applications, devices, databases and business systems. Companies use it for reporting, exploratory analysis, machine learning, streaming workloads and long-term data retention.
A Data Lake accepts varied data formats and often applies structure when the data is read, while a warehouse usually models and validates data before loading it. A lake suits flexible exploration and large-scale processing; a warehouse often offers simpler governed reporting. Many modern architectures use both.
A Data Lake becomes part of a lakehouse architecture when table formats and governance add features such as transactions, schema control and reliable updates. Technologies such as Delta Lake, Apache Iceberg and Apache Hudi support this approach. It combines open storage with more warehouse-like behavior.
A strong Data Lake specialist usually understands cloud storage, SQL, Python or Scala, distributed processing and orchestration. Useful adjacent skills include Apache Spark, Kafka, dbt, Airflow, infrastructure-as-code, data modeling, cataloging, security and observability.
A Data Lake project needs a professional who has handled the relevant data sources, workloads and operating model, not just configured storage. A focused ingestion task may need a narrower specialist, while a new platform requires architecture, governance, security and production operations experience.
A Data Lake engagement can often be delivered remotely because architecture, pipeline development and documentation work well through shared repositories and cloud environments. On-site workshops can still help with discovery, access decisions and stakeholder alignment. Teams in Switzerland should agree on collaboration methods and language expectations early.
A capable Data Lake professional can explain partitioning, schema evolution, data quality, lineage, recovery and access control in practical terms. Ask for examples of production pipelines, failure handling and documentation, then assess whether the proposed design fits your sources, users and operating constraints.
Before engaging a Data Lake specialist, clarify the business outcomes, source systems, expected consumers, cloud environment and ownership after delivery. Also define security requirements, governance responsibilities, existing tools and whether the work involves a new platform, migration or remediation of an existing lake.
The average hourly rate of freelancers in Switzerland who have used Data Lake in their recent projects is 102 €, which corresponds to a daily rate of about 814 € based on an 8-hour working day.
Of the freelancers in Switzerland who have used Data Lake in their recent projects, 100% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 33% hold a doctorate.
On average, freelancers in Switzerland who have used Data Lake in their recent projects have 27 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers in Switzerland who have used Data Lake in their recent projects are English (100%), German (67%), and French (33%).
The most common industries among freelancers in Switzerland who have used Data Lake in their recent projects are Banking and Finance (100%), Information Technology (100%), and Manufacturing (67%).
The most common business areas among freelancers in Switzerland who have used Data Lake in their recent projects are Information Technology (100%), Business Intelligence (83%), and Project Management (83%).
Main locations of FRATCH Experts, who have recently used Data Lake
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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FRATCH CEO
