Data Analyst for Predictive Maintenance Dashboards (Contract)

NES Fircroft; Auvergne-Rhône-Alpes; On-site; EUR 42,000 - 54,000; Full time

Job summary

NES Fircroft in Grenoble, France, invites applications for a Data Analyst on a 3 to 6 months contract starting 15th October 2026. You will transform raw telemetry and manual data into decision-support tools to optimize maintenance performance, using Power BI, cross-database techniques, and AI-assisted analytics.

You will develop dashboards, KPI reporting, and a user-friendly AppSheet interface for field data collection, under the Reliability team, with an emphasis on predictive maintenance and

Qualifications

• Data analysis, Power BI, process improvement and supervision are required.
• Develop algorithmic approaches for cross-referencing several heterogeneous databases.
• Identify common criteria to merge industrial data flows and design KPIs and dashboards.
• Use of AI tools such as Gemini or Gemini Notebook.

Responsibilities

• Analyze telemetry data and valorise it for decision support.
• Develop cross-referencing methods to merge data from multiple databases.
• Design KPIs and dashboards for performance management.
• Implement predictive maintenance with weak signal detection.
• Develop low-code interfaces with AppSheet for field data collection and monitoring.
• Automate downtime reporting and create visualizations of failure distributions.

Skills

Data Analysis

Power BI

Process improvement

Supervision

AI tools

KPI design

Cross-database integration

Tools

Gemini Notebook

AppSheet

Job description

NES Fircroft in Grenoble, France, invites applications for a Data Analyst on a 3 to 6 months contract starting 15th October 2026. You will transform raw telemetry and manual data into decision-support tools to optimize maintenance performance, using Power BI, cross-database techniques, and AI-assisted analytics.

You will develop dashboards, KPI reporting, and a user-friendly AppSheet interface for field data collection, under the Reliability team, with an emphasis on predictive maintenance and