Data Warehouse Architect - SQL Server & ETL Expert

Stadler Digital Labs; Lisboa; Presencial; EUR 55 000 - 90 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Fringe benefits

Training and growth opportunities

Resumo da oferta

Stadler Digital Labs, the digital competence center for Stadler Group, seeks an experienced Data Warehouse Engineer in Lisbon, Portugal. You will design, develop, and optimize data warehouses and ETL pipelines using MS SQL Server, SSIS/SSRS/SSAS, and T‑SQL, collaborating with ERP systems and diverse databases.

You will drive data governance, deployment automation, and documentation, sharing knowledge across teams while staying curious about AI-enabled enhancements to data workflows.

Qualificações

• Academic background in Engineering or similar.
• 5+ years of relevant professional experience in data warehouse architecture and automation.

Responsabilidades

• Design, develop, and optimize data warehouse solutions.
• Build and optimize ETL pipelines.
• Ensure data quality, governance, and architecture alignment.
• Collaborate with ERP and database teams (SAP, Abacus, INFOR) for data integration.
• Deploy automation and configuration across development, test and production.
• Produce technical documentation and share knowledge across teams.

Conhecimentos

Data warehouse architecture

ETL design & optimization

Microsoft SQL Server

SSIS/SSRS/SSAS

T‑SQL

Python scripting

PowerShell scripting

Data governance

Automation & deployment

Technical documentation

Formação académica

Engineering or equivalent

Ferramentas

Microsoft SQL Server

SSIS

SSRS

SSAS

Oracle

MariaDB

PostgreSQL

Progress

Descrição da oferta de emprego

Stadler Digital Labs, the digital competence center for Stadler Group, seeks an experienced Data Warehouse Engineer in Lisbon, Portugal. You will design, develop, and optimize data warehouses and ETL pipelines using MS SQL Server, SSIS/SSRS/SSAS, and T‑SQL, collaborating with ERP systems and diverse databases.

You will drive data governance, deployment automation, and documentation, sharing knowledge across teams while staying curious about AI-enabled enhancements to data workflows.