Data Pipeline Experts in Switzerland
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Meet FRATCH Experts in Switzerland, who have recently used Data Pipeline
Arthur J.
Last position:
Principal AI Consultant & Founder at Modus Data GmbH
Independent Swiss data science and applied AI consultancy: strategy, architecture, and hands-on delivery of custom AI, LLM, and MLOps projects. Available for fractional advisory, architectural audits, and end-to-end project delivery.
Alejandro A.
Last position:
AI Researcher & Engineer at Tufa Labs
- Deployed and optimized the inference stack on a multi-node DGX B200 cluster across vLLM and SGLang (serving, throughput and latency tuning).
- Built, with a small team, an internal Python library for LM pretraining covering the full training loop: distributed training with PyTorch FSDP, data pipelines, checkpointing, config and hyperparameter management, and experiment tracking.
- Built and evaluated agent scaffolds on interactive game benchmarks similar to ARC-AGI-3, with metrics for how models plan, explore and adapt across multi-step episodes; classified model errors and fed the findings back into scaffold and evaluation design.
- Researched looped transformer architectures.
Gwang Jin K.
Last position:
Data Scientist / Applied AI, Automation & Data Systems Researcher at Independent
- Built and explored applied GenAI, RAG, GraphRAG, local LLM, agentic AI and document-intelligence prototypes for structured analysis, evidence extraction, semantic search, technical reasoning and decision-useful reporting
- Developed private local-LLM workflows and AI system patterns focused on privacy, reproducibility, reviewability, low-cost inference and practical user control
- Built reproducible Python/R workflows for data analysis, automation, API-driven tooling, validation logic, technical documentation and AI-assisted software development
- Designed workflows around explicit assumptions, traceable inputs, reviewable outputs and failure-mode awareness rather than black-box “looks good” demonstrations
- Supported RAHN AG in a chemical/regulatory environment with data extraction and processing around WERCS, a regulatory application for chemical product and compliance data
- Explored complex application/database schemas and wrote nested SQL queries to extract information for mixture calculations, component relationships, regulatory rules and reporting logic
- Continued hands-on development in Git/GitHub/GitLab/Bitbucket, Docker/Linux deployment patterns, REST/API workflows, error handling, technical writing and fast AI-assisted prototyping
- Built technical writing and documentation workflows that turn complex systems into clear runbooks, checklists, decision notes and user-facing explanations
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).
Noel F.
Last position:
Career Transition
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.
Discover over 15,000 top freelancers
Statistics of experts using Data Pipeline
Aggregated from the professional profiles of matched freelancers.
Experience
16 years
Position duration
2.5 years
Positions per freelancer
13
Top business areas
Information Technology, Business Intelligence, Research and Development
Top industries
Information Technology, Banking and Finance, Manufacturing
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
17%
Certifications per freelancer
4
Most common languages
English, German, Spanish
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 Pipeline
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 Pipeline 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 (100%)
- Banking and Finance (67%)
- Manufacturing (67%)
- Education (50%)
- Automotive (33%)
- Energy (33%)
- Healthcare (33%)
- Insurance (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Data Pipelines Do
A Data Pipeline moves information from source systems to destinations where it can be analysed, stored or used by applications. It can ingest event streams, files, database changes or API responses, then validate, transform and load data into warehouses, lakes or operational systems. Reliable pipelines make information available without repeated manual work.
Core Architecture
Pipeline design covers batch and streaming flows, source connectors, schemas, transformations, orchestration and monitoring. Specialists choose patterns that fit data volume, delivery speed, recovery needs and governance requirements. Common outputs include curated warehouse tables, lakehouse layers, feature datasets and feeds for reporting or machine learning.
Ecosystem and Tooling
- Connect databases, SaaS applications, APIs, files and message brokers
- Orchestrate workflows with Apache Airflow, Dagster or Prefect
- Transform data with SQL, dbt, Spark or Python
- Stream events through Kafka, Flink or cloud-native services
- Load governed data into Snowflake, BigQuery, Databricks or similar systems
The surrounding stack may include Docker, Kubernetes, Terraform, Git-based delivery and cloud services from AWS, Azure or Google Cloud. Strong specialists understand how these components behave together rather than treating each tool in isolation.
When Companies Need Help
Companies often bring in freelance expertise when a new warehouse or lakehouse must be populated, fragile jobs need to be replaced, or teams need dependable streaming. Other signals include repeated pipeline failures, unclear data ownership, slow refreshes and transformations that cannot be tested safely. In Switzerland, projects may also span multilingual teams, regulated industries and hybrid environments, making clear documentation and remote collaboration important.
Delivery Responsibilities
A professional may assess existing sources, define contracts, map dependencies and establish quality checks before building the flow. Deliverables can include ingestion services, transformation models, orchestration schedules, deployment configuration, lineage documentation and operational runbooks. They should also plan retries, idempotency, backfills, alerting and safe handling of changing schemas.
What Strong Experts Show
Look for evidence of production pipelines that remained understandable and recoverable under change. Strong professionals explain trade-offs between batch and streaming, prove data quality with meaningful tests and make failures visible to the right people. They can discuss cost control, access permissions, observability and disaster recovery in practical terms, while adapting their communication to product, analytics and infrastructure teams.
Frequently asked questions
Everything clients usually want to know about Data Pipeline, in one place.
A Data Pipeline collects information from sources such as databases, APIs, applications and event streams, then moves and transforms it for a target system. Companies use pipelines to supply analytics, operational workflows, reporting, machine learning and data products with consistent information.
A Data Pipeline is the broader flow of moving and processing data, while ETL and ELT describe where transformation happens relative to loading. ETL transforms before the destination, whereas ELT loads first and transforms inside the warehouse or lakehouse. A pipeline can use either pattern, or combine both.
A strong Data Pipeline specialist usually understands SQL, Python, data modelling, cloud storage, APIs and message systems. Useful adjacent skills include Apache Airflow, dbt, Spark, Kafka, Docker, Terraform, observability and access management. Knowledge of warehouse or lakehouse design is also valuable.
The right level depends on source complexity, delivery expectations, data sensitivity and the consequences of failure. A straightforward batch flow may need focused implementation experience, while streaming, migrations or regulated workloads require a professional who has handled orchestration, recovery, quality controls and operational ownership in comparable settings.
Yes, much Data Pipeline work can be delivered remotely when systems, credentials and environments are accessible through secure procedures. On-site sessions can still help with discovery, architecture decisions or workshops involving sensitive data. Agree on communication routines, documentation, time-zone overlap and language expectations before work begins.
A Data Pipeline should use streaming when users or systems need events with low delay, such as fraud checks, live monitoring or operational triggers. Batch is often simpler and more economical for scheduled reporting, large periodic loads and processes without immediate action. The choice should follow business latency and recovery needs, not fashion.
Ask the Data Pipeline specialist to explain a real design, including failure handling, schema changes, testing, backfills, monitoring and access controls. Review whether their documentation is clear and whether they measure freshness, completeness and correctness. A strong professional can state trade-offs plainly and show how the system is operated after delivery.
Before starting, a Data Pipeline freelancer should clarify source systems, target models, refresh or latency needs, ownership, security constraints and acceptance criteria. They should also confirm deployment access, existing orchestration, data quality expectations and who responds to incidents. These details prevent a technically sound flow from failing in daily operations.
The average hourly rate of freelancers in Switzerland who have used Data Pipeline in their recent projects is 94 €, which corresponds to a daily rate of about 752 € based on an 8-hour working day.
Of the freelancers in Switzerland who have used Data Pipeline in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Switzerland who have used Data Pipeline in their recent projects have 16 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 Data Pipeline in their recent projects are English (100%), German (50%), and Spanish (33%).
The most common industries among freelancers in Switzerland who have used Data Pipeline in their recent projects are Information Technology (100%), Banking and Finance (67%), and Manufacturing (67%).
The most common business areas among freelancers in Switzerland who have used Data Pipeline in their recent projects are Information Technology (83%), Business Intelligence (67%), and Research and Development (67%).
Main locations of FRATCH Experts, who have recently used Data Pipeline
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
