Senior MIS & Analytics Engineer (Databricks, Power BI)

Al Hilal Bank

Abu Dhabi

On-site

AED 300,000 - 550,000

Full time

6 days ago
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Job summary

Al Hilal Bank seeks an experienced MIS Specialist to lead enterprise reporting, data analytics, and regulatory reporting solutions. You will design and implement data pipelines, dashboards, and governance across banking data assets, using Databricks, SQL, Power BI, and Power Platform.

The ideal candidate has 8+ years in data, MIS, reporting, analytics, and a strong banking sector background, with Azure knowledge and proficiency in DAX and Python preferred.

Qualifications

  • 8+ years of relevant experience in data, MIS, reporting, and analytics.
  • Strong analytical and problem-solving skills.
  • Experience with data warehousing and ETL processes.
  • Hands-on with Databricks, SQL, Power BI, and Power Platform tools.

Responsibilities

  • Design, develop, and maintain enterprise MIS and reporting solutions.
  • Build and optimize data pipelines using Databricks and SQL.
  • Develop interactive dashboards and analytical reports using Power BI.
  • Automate business processes via Power Automate and Power Apps.
  • Support data warehousing, ETL processes, and reporting frameworks.
  • Collaborate with stakeholders to gather reporting requirements.
  • Ensure data accuracy, integrity, and governance across platforms.
  • Support regulatory reporting initiatives.

Skills

Analytical thinking
Problem solving
Excel VBA
Stakeholder engagement

Tools

Databricks
SQL
Power BI
Power Automate
Power Apps
Azure Cloud
DAX
Python
ETL

Job description

Al Hilal Bank seeks an experienced MIS Specialist to lead enterprise reporting, data analytics, and regulatory reporting solutions. You will design and implement data pipelines, dashboards, and governance across banking data assets, using Databricks, SQL, Power BI, and Power Platform.

The ideal candidate has 8+ years in data, MIS, reporting, analytics, and a strong banking sector background, with Azure knowledge and proficiency in DAX and Python preferred.

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