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Performance Data Analyst

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Onsite

Performance Data Analyst

enlight

Performance Data Analyst

Skills

SQL QueriesPythonData AnalysisAIPower BIAzure

13 days ago

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Important information


Contract type:

Permanent

Location:

Rosh Haayin, Israel

Work mode:

Onsite

Published on:

11 October 2026

What they need


Join Enlight’s Data & Analytics team and turn complex operational data into actionable insights that improve the performance of our global renewable energy portfolio across PV, BESS, and Wind. You’ll analyze SCADA, meteorological, metering, and market data to identify trends and anomalies, build meaningful KPIs and dashboards, and support data-driven decision-making. The role combines hands-on data analysis with data integrations, automation, and AI-driven workflows to deliver reliable and scalable analytical solutions.

Requirements:

  • B.Sc. in Computer Science, Information Systems, Engineering, or a related field.
  • 2–3 years of experience in data analytics, analytics engineering, or a related role.
  • Strong SQL skills and hands-on experience with Python.
  • Experience with BI tools (preferably Power BI), including building well-structured dashboards and reports.
  • Experience working with ETL/ELT processes, APIs, and data integrations.
  • Hands-on experience using AI tools for data analysis, coding, automation, or agent-based workflows.
  • Familiarity with cloud data platforms (preferably Microsoft Azure) – an advantage.
  • Analytical, detail-oriented mindset with strong problem-solving and communication skills.

Responsibilities:

  • Analyze real-time and historical data to identify performance trends, anomalies, and inefficiencies, and support root-cause analysis.
  • Design and build reports, dashboards, and visualizations to drive data-driven decision-making.
  • Define, model, and maintain KPIs used to measure asset performance.
  • Ensure data quality and reliability through validation, monitoring, and detection of data gaps and sensor faults.
  • Work with ETL/ELT pipelines, APIs, and external data sources to support analytical needs.
  • Build automated and AI-driven workflows to improve analysis, monitoring, and operational efficiency.
  • Collaborate with technical teams, asset managers, and stakeholders to improve portfolio performance.

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