Senior Data Platform Engineer
Summary
Senior Data Platform Engineer at Avaloq in Tessin, Switzerland, designing, building, and operating a secure, governed data platform for security, audit, operational, and analytics workloads. Daily work spans batch and streaming pipelines, data modeling, quality and governance controls, and integration with SIEM, monitoring, and BI platforms, using Python, SQL, and Terraform.
We are seeking a Senior Data Platform Engineer to design, build, and operate a scalable, secure, and governed data platform supporting security, audit, operational, and analytics workloads.
This role is primarily focused on data engineering and data platform development, including data architecture, ingestion pipelines, data modeling, quality controls, governance, and integration with analytics and security platforms. The successful candidate will have strong experience designing and operating data platforms, building reliable batch and streaming pipelines, and managing large volumes of structured and semi-structured data.
The role requires close collaboration with security, infrastructure, and application teams to deliver trusted, high-quality data that enables monitoring, reporting, threat detection, compliance, and business insights.
Your key tasks
Data Platform Architecture
- Design and evolve scalable data platform architectures for security, audit, operational, and analytics data
- Define data storage strategies, schemas, data models, partitioning, retention, and lifecycle management approaches
- Evaluate and prototype new technologies and architectures to improve scalability, performance, and cost efficiency
Data Engineering & Pipelines
- Design, build, and maintain batch and streaming data pipelines
- Develop robust ingestion frameworks for logs, audit data, application events, security telemetry, and operational datasets
- Implement data transformation, enrichment, normalization, correlation, and aggregation processes
- Ensure pipelines are reliable, scalable, observable, and resilient
Data Modeling & Storage
- Design relational, analytical, and event-based data models
- Optimize database structures, query performance, indexing, and storage efficiency
- Support the implementation of data lake, warehouse, and lakehouse concepts where appropriate
Data Quality & Governance
- Define and implement data quality controls across ingestion and transformation layers
- Develop validation, reconciliation, deduplication, and completeness checks
- Support data lineage, metadata management, ownership, retention, auditability, and regulatory requirements
- Implement controls for sensitive and regulated data
Platform Integration & Analytics Enablement
- Integrate data from diverse internal and external platforms, applications, databases, APIs, and messaging systems
- Deliver curated datasets that support reporting, analytics, observability, compliance, and security operations
- Support integration with SIEM, monitoring, and business intelligence platforms
- Collaborate with analytics and reporting teams to improve data accessibility and usability
Engineering & Automation
- Develop data engineering services, tooling, and automation using Python and SQL
- Contribute to CI/CD practices for data platform components
- Support infrastructure automation where required, using Terraform and related tooling
- Maintain engineering standards, documentation, and operational procedures