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Salary
$23k – $52k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Senior · 5+ years exp
Overview
Company
Impact
Profile match
Nxtinnov is an enterprise AI and data transformation company that provides AI strategy, data modernization, intelligent automation, AI development, governance, and scalable AI infrastructure for businesses.

We are looking for a hands-on Senior Data Analyst to work on large-scale smart metering and energy consumption data for a UK energy-sector project.

The role involves analyzing large and complex datasets, identifying trends, patterns, anomalies, and data-quality issues, and converting data into meaningful business insights. You will also take ownership of Power BI dashboards, KPI reporting, and analytical outputs while working closely with business and operational stakeholders.

Responsibilities:

  • Analyze large volumes of smart-metering and energy consumption data.
  • Use SQL to independently query, investigate, and analyze datasets.
  • Perform data profiling, validation, reconciliation, and data-quality analysis.
  • Identify trends, patterns, anomalies, outliers, and exceptions.
  • Develop and maintain Power BI dashboards and KPI reports.
  • Translate business questions into structured analytical approaches.
  • Present analytical findings and actionable insights to stakeholders.
  • Work closely with business and operational teams to understand requirements.
  • Guide and review the work of other data analysts.

Requirements:

  • 5+ years of hands-on data analytics experience.
  • Strong SQL skills.
  • Strong hands-on experience with Power BI.
  • Good Excel skills.
  • Experience with data profiling, validation, cleansing, and reconciliation.
  • Strong analytical and problem-solving skills.
  • Experience working with large datasets.
  • Good stakeholder management and communication skills.
  • Ability to convert data findings into clear business insights.

Good to Have:

  • Experience in Energy / Utilities / Smart Metering.
  • Experience with smart meter or half-hourly consumption data.
  • Exposure to meter reads, billing, or settlement data.
  • Python experience for complex analysis or large datasets.
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