The Senior Analytics Engineer is responsible for designing and leading the development of scalable, high-quality analytics data platforms that enable advanced analytics, business intelligence, and AI use cases. This role plays a strategic function in defining data modelling standards, ensuring data reliability, and aligning analytics engineering practices with organisational data strategy.
Responsibilities
Analytics Data Modelling & Architecture
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Lead the design, development, and optimisation of scalable analytics data models and data layers.
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Define and enforce modelling standards, best practices, and architectural patterns across the organisation.
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Ensure consistent, reusable, and well-governed definitions of metrics, dimensions, and business logic.
Data Quality, Reliability & Observability
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Establish and oversee robust data quality frameworks, testing strategies, and monitoring systems.
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Ensure reliability and performance of data pipelines and analytical models at scale.
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Drive root cause analysis and resolution of complex data issues across upstream and downstream systems.
Enablement of Advanced Analytics & BI
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Partner with data scientists, analysts, and BI teams to enable advanced analytics and AI use cases.
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Design data models that support scalability, performance, and flexibility for diverse analytical needs.
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Champion self-service analytics by creating well-structured, accessible, and performant datasets.
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Expert-level SQL and advanced data modelling expertise.
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Deep experience with modern data stack tools and architectures.
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Strong understanding of data pipelines, orchestration, and dependencies.
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Familiarity with software engineering best practices in data (CI/CD, testing, modular design).
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Ability to design scalable solutions to complex data challenges.
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Strong critical thinking and data validation skills.
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Proactively identifies risks, inefficiencies, and improvement opportunities.
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Define and implement analytics engineering standards, governance frameworks, and best practices.
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Lead documentation initiatives for data models, definitions, and lineage.
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Ensure compliance with data governance, security, and regulatory requirements.
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Establish and oversee robust data quality frameworks, testing strategies, and monitoring systems.
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Ensure reliability and performance of data pipelines and analytical models at scale.
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Drive root cause analysis and resolution of complex data issues across upstream and downstream systems.
Qualifications
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Matric and a Bachelor's degree in Data Science, Statistics, Computer Science, Information Systems, Engineering, or related field.
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5+ years of experience in analytics engineering, data engineering, or advanced analytics roles.
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Expert-level proficiency in SQL and extensive experience designing scalable analytical data models.
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Strong experience with data transformation tools (e.g., dbt) and modern data warehouses (e.g., BigQuery, Snowflake, Redshift).
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Deep understanding of data modelling concepts (star schema, dimensional modelling, data vault, fact/dimension design).
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Proven experience working with BI tools (Power BI, Tableau, Looker etc.) and enabling self-service analytics.
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Strong experience with version control systems (e.g., Git) and CI/CD practices in data workflows.
Advantageous
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Postgraduate qualification in a related field.
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Experience in retail, healthcare, or financial services data environments.
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Strong experience with cloud platforms (AWS, Azure, GCP).
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Exposure to machine learning pipelines and feature engineering.
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Experience mentoring or leading analytics/data teams.
