Permanent

Gross annual salary: €30.000 - €40.000

Intermediate (2 - 4 years)

Requires work permit

Languages: Required: English

WHO WE ARE?
Being the daughter company of Swisscom - leading telecommunications company and one of the most sustainable and innovative companies in Switzerland - we share the same values, vision and goals.As a trustworthy and efficient Swisscom IT service partner, we create valuable customer experiences. We work in strong collaboration with our partners in Switzerland and attract and retain high performers.

We're expanding our footprint in Lisbon and are looking for a Data & Integration Architect to join our growing AI team.We invite driven professionals to join us, grow with us, and help shape the future.
The team / projectThe Mobile Technology & Voice Value Stream is building an AI tooling suite that automates and supports the operation of Swisscom's mobile and voice network. We are a small, collaborative pod working closely with product owners and business stakeholders in Switzerland, network SMEs and external specialists, and together we're building an internal AI delivery capability for Swisscom Operations.

As Data & Integration Architect, you own the integration layer of the AI tooling suite and turn operational telecommunications data into reliable, governed and AI-ready capabilities. In this hands-on role, you design integration patterns, build source adapters and retrieval tools, and define data contracts that connect operational platforms, data products, telemetry sources and external services to the agent ecosystem.

The initial integration scope may include operational network-performance platforms, active testing systems, customer-experience signals, crowd-sourced measurements, incident context and future Swisscom data products.

What you'll do

• Define the integration strategy: Set reusable patterns for operational data sources and reduce the effort needed to onboard future platforms.
• Design the interfaces: Design APIs, adapters, data contracts, authentication flows and failure-handling patterns, so agents continue safely when individual sources fail.
• Build retrieval tools: Build and maintain source-specific retrieval tools and agent integrations, normalizing heterogeneous responses while preserving provenance and meaning.
• Engineer reliable data access: Develop data-access components using APIs, events, files or streams, with schemas, validation, caching, freshness controls, rate-limit handling and data-quality monitoring.
• Enable incident investigations: Represent timestamps, regions, services, incidents, KPIs and confidence metadata consistently, and support time-window and cross-source correlation.
• Build trust in the data: Work with data owners on ownership, access, classification and retention, document lineage and limitations, and make sure every agent claim traces back to its source.
• Make data agent-ready: Expose operational capabilities as safe, well-defined tools, optimize retrieval for cited responses, and contribute to evaluation datasets and test scenarios.
• Own the documentation: Maintain technical documentation and Architecture Decision Records, capture KPI interpretation rules with network SMEs, and support incidents involving upstream sources.
• Grow internal know-how: Pair with external data and integration specialists during the MVP and pilot.
  
  

Job requirementsData Engineering & Integration

• Strong experience in data engineering, systems integration or integration architecture.
• Hands-on API development and integration experience in Python or a comparable language.
• Experience with REST/JSON, authentication, secrets, rate limits, error handling and observability.
  
  

Data Engineering

• Experience designing schemas, data contracts, validation and normalization patterns.
  
  

Cloud

• Practical cloud experience, preferably AWS.
  
  

Soft Skills

• Strong communication skills in English (written and spoken).
• Curiosity and the ability to investigate unfamiliar platforms and safely expose their capabilities to other systems.
• Collaborative approach when working with network SMEs, data owners and platform owners.
• Strong ownership mindset and care for clear, maintainable documentation.
• Willingness to share knowledge so integrations stay operable without long-term external dependency.
  
  

Nice to have

• Telecommunications, OSS/BSS, network telemetry, observability or incident-management experience.
• ODP, data products, streaming technologies or operational time-series data.
• Building tools or retrieval interfaces for LLM and agent applications.
• Understanding of data lineage, governance, privacy and enterprise security controls.
• nGeniusONE, SIGOS, customer-experience data or similar operational platforms.