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Business Intelligence Analyst

Absa Group

🇬🇧 English
Power BI DAX ETL ELT Data modelling Snowflake schema Data pipelines Data governance Data quality checks Data dictionaries

Job description

About the role

The Business Intelligence Analyst will turn raw data into actionable insights that drive strategic decisions and operational efficiency for a leading African bank. You will work closely with business stakeholders, IT, and cloud teams to deliver high‑impact analytics, visualisations and data‑driven recommendations.

Key responsibilities

  • Partner with business units to capture analytical requirements and translate them into BI and advanced analytics solutions.
  • Develop, maintain and optimise Power BI dashboards, reports and executive visualisations.
  • Define, manage and monitor KPIs covering revenue, profitability, customer acquisition, credit portfolio performance and liquidity.
  • Perform deep‑dive analyses, root‑cause investigations and variance analysis for financial and operational performance.
  • Design, build and maintain scalable ETL/ELT data pipelines from multiple source systems.
  • Create and optimise star and snowflake data models for reporting and analytics.
  • Ensure data accuracy, consistency, governance and compliance with privacy and regulatory standards.
  • Implement data quality checks, validation rules, data dictionaries, lineage and metadata documentation.
  • Manage Power BI datasets, refresh schedules, Row‑Level Security and deployment pipelines.
  • Act as a trusted analytics advisor, training and supporting users on BI tools and data interpretation.

Required profile

  • Proven experience delivering BI solutions in a banking or financial services environment.
  • Strong analytical mindset with the ability to translate business needs into technical specifications.
  • Excellent communication skills for stakeholder engagement and user training.
  • Ability to work collaboratively with IT, cloud and data engineering teams.

Required skills

  • Power BI
  • DAX
  • ETL / ELT processes
  • Data modelling (star and snowflake schemas)
  • Data pipelines
  • Row‑Level Security (RLS)
  • Data governance
  • Data quality checks
  • Data dictionaries and metadata management

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