Build Scalable Data Infrastructure for Your Business
Expert data engineering freelance consultant in Switzerland. I design and build robust data pipelines, ETL processes, and data warehouses that power your analytics and ML systems. Specializing in cloud-native architectures on AWS, Azure, and GCP for Swiss businesses.
Scalable data pipelines handling millions of records
Cloud-native solutions on AWS, Azure, GCP
Expertise in modern data stack (dbt, Airflow, Spark)
Real-time and batch data processing
Data quality and monitoring frameworks
Cost optimization for data infrastructure
DataOps best practices and automation
Experience with Swiss data compliance requirements
Switzerland-based with flexible engagement models
Build robust, scalable data pipelines for ETL/ELT processes. Handle batch and real-time data ingestion from multiple sources with proper error handling and monitoring.
Design and implement modern data warehouses on Snowflake, BigQuery, or Redshift. Optimize schemas, implement partitioning, and ensure query performance.
Design cloud-native data architectures on AWS, Azure, or GCP. Leverage managed services for cost-effective, scalable solutions.
Build data lakes for storing structured and unstructured data at scale. Implement proper cataloging, governance, and access controls.
Implement real-time data processing with Kafka, Kinesis, or Pub/Sub. Build systems for event-driven architectures and real-time analytics.
Implement data quality checks, validation rules, and monitoring systems. Ensure data reliability and catch issues before they impact downstream systems.
Data engineering focuses on building and maintaining the infrastructure that collects, stores, and processes data. Data science uses that data to extract insights and build models. I handle the engineering side, ensuring your data scientists have reliable, high-quality data to work with.
Each platform has strengths: AWS offers the most comprehensive services, Azure integrates well with Microsoft ecosystem, and GCP excels at big data and ML. I'll recommend the best fit based on your existing infrastructure, team skills, and specific requirements.
I implement multiple layers of validation: schema validation, data profiling, anomaly detection, and business rule checks. I also set up monitoring and alerting so issues are caught and resolved quickly.
Yes! I have extensive experience integrating modern data platforms with legacy systems. I can build connectors and transformation layers that allow you to modernize gradually without disrupting existing operations.
Simple pipelines can be built in 1-2 weeks, while comprehensive data platforms with multiple sources and destinations take 2-4 months. I typically start with a critical use case to deliver value quickly, then expand.
Security and compliance are built into every data engineering project. I implement encryption at rest and in transit, proper access controls, audit logging, and ensure compliance with GDPR and Swiss data protection laws.