Senior Data Engineer

3d3 days ago

Miele

Braşov, RO · Full-time · RON 95,000 – RON 135,000

About this role

We are looking for a Senior Data Engineer to ensure the stable, SLA-compliant, and governance-compliant operation of central data products for device usage data on Azure. The role focuses on production operations, incident handling, and reproducible deployments while integrating new device generations.

Daily work centers on hands-on management of Databricks, PySpark, Azure DevOps, and Unity Catalog. You will maintain operational stability through monitoring, alerting, root cause analysis, and controlled change processes that protect data quality and SLAs.

You will apply strong operational ownership across reliability, access governance, documentation, and data quality controls. The environment values structured, quality-conscious execution using the Atlassian Suite alongside AI tools that improve productivity while requiring critical validation of outcomes.

This position suits professionals seeking long-term career growth at a best-in-class company where operational excellence and maintainability are appreciated and rewarded.

Requirements

4+ years of hands-on production experience with Databricks, PySpark, SQL, and Azure DevOps or alike technologies
4+ years of experience operating SLA-driven data pipelines, including incident handling and root cause analysis
4+ years of experience with Unity Catalog, access governance, and data quality controls
4+ years of experience implementing monitoring, alerting, and operational stability measures
Operationally driven mindset focused on stability, reliability, SLAs, and long-term maintainability
Hands-on ownership to resolve incidents, improve pipelines, and drive topics into production
Structured and quality-conscious approach valuing governance, documentation, reproducibility, and controlled change

Responsibilities

Ensure stable, SLA-compliant, and governance-compliant operation of central data products for device usage data on Azure
Handle incidents and perform root cause analysis for SLA-driven data pipelines
Execute reproducible deployments and integrate new device generations without compromising stability
Implement monitoring, alerting, and operational stability measures across data products
Maintain access governance, data quality controls, and documentation standards
Apply AI tools to improve productivity, quality, and analysis while validating final outcomes