Data Architect

About the role

We are seeking a senior Data Architect to join Ericsson’s Data & AI Solutions team within the Strategy & Transformation department. You will lead the design and implementation of enterprise‑scale data platforms that support digital transformation and Industry 4.0 initiatives.

Key responsibilities

• Design and build enterprise‑scale data platforms, owning end‑to‑end data integration, transformation pipelines and platform components across hybrid and multi‑cloud environments.
• Architect highly scalable, resilient, and cost‑efficient data solutions using modern cloud technologies, while remaining hands‑on in platform development.
• Evaluate architectural approaches and tools against business requirements, considering trade‑offs and the broader technology landscape.
• Implement data governance frameworks, standards and policies for data quality, lineage, security and regulatory compliance.
• Challenge assumptions and sub‑optimal solutions, applying critical and systems thinking to deliver genuine business value.
• Provide technical guidance and mentorship to data engineers, promoting strong engineering practices and architectural thinking.
• Collaborate with technical and business stakeholders to translate requirements into scalable platform designs and clearly present trade‑offs.
• Drive continuous improvement through automation, optimized data delivery and enhanced tooling to increase efficiency and reduce operational overhead.

Required profile

• 5+ years of hands‑on experience in data engineering and data platform architecture, with proven delivery of enterprise‑level data solutions in production.
• Deep technical expertise in designing scalable data platforms, data modelling and software architecture.
• Experience with DataOps and DevOps practices, including CI/CD, Infrastructure as Code, policy as code, automated testing, deployment and environment management.

Required skills

• Azure cloud platform
• Snowflake data warehouse
• dbt (or equivalent transformation framework)
• CI/CD pipelines
• Infrastructure as Code
• Policy as Code
• Automated testing and deployment
• DataOps practices
• DevOps practices