Manufacturing Data Engineer — MES Pipelines & AI Insights

Ephos; Lombardia; On-site; EUR 40,000 - 65,000; Full time

Benefits offered by this job

Stock options

Competitive salary €40k-€65k per year

Growth opportunities

Job summary

Ephos in Lombardia, Italy, is building a new Manufacturing Execution System (MES) to track production from raw glass to finished photonic IC. You will own the data infrastructure end to end and create pipelines that feed data science models, dashboards, and monthly metrics.

The role requires strong SQL and Python, experience with data pipelines in industrial settings, and a dedication to on-site full-time work.

Qualifications

• Bachelor's degree in CS or related field.
• Experience building production data pipelines from raw or sensor sources into structured systems.
• Strong SQL and Python; experience with pipeline orchestration and schema design.
• Practical experience with data quality frameworks and validation at ingestion.
• Experience with data versioning or lineage tooling is a plus.
• Willingness to commit to full-time on-site work.

Responsibilities

• Build and maintain data pipelines from production lines to the MES and shape MES structure.
• Create downstream pipelines turning MES data into datasets, models, dashboards, and monthly metrics.
• Set up data versioning and lineage tracking to trace data versions to models.
• Design labeling/annotation workflow for defect images and data.
• Build a central knowledge base for LLM querying across documents, chats, code repositories, logs, and MES data.

Skills

SQL

Python

Data pipelines

Data quality

Data versioning

On-site work

Education

Bachelor's degree in computer science or related field

Job description

Ephos in Lombardia, Italy, is building a new Manufacturing Execution System (MES) to track production from raw glass to finished photonic IC. You will own the data infrastructure end to end and create pipelines that feed data science models, dashboards, and monthly metrics.

The role requires strong SQL and Python, experience with data pipelines in industrial settings, and a dedication to on-site full-time work.