Role Purpose:

Requirements

Minimum

• Bachelor’s degree in Data Science, Statistics, Computer Science, Information Systems, Engineering, or related field.
  
• 5+ years of experience in analytics engineering, data engineering, or advanced analytics roles.
  
• Expert-level proficiency in SQL and extensive experience designing scalable analytical data models.
  
• Strong experience with data transformation tools (e.g., dbt) and modern data warehouses (e.g., BigQuery, Snowflake, Redshift).
  
• Deep understanding of data modelling concepts (star schema, dimensional modelling, data vault, fact/dimension design).
  
• Proven experience working with BI tools (Power BI, Tableau, Looker etc.) and enabling self-service analytics.
  
• Strong experience with version control systems (e.g., Git) and CI/CD practices in data workflows.
  

Responsibilities

Analytics Data Modelling & Architecture

• Lead the design, development, and optimisation of scalable analytics data models and data layers.
  
• Define and enforce modelling standards, best practices, and architectural patterns across the organisation.
  
• Ensure consistent, reusable, and well-governed definitions of metrics, dimensions, and business logic.
  

Data Quality, Reliability & Observability

• Establish and oversee robust data quality frameworks, testing strategies, and monitoring systems.
  
• Ensure reliability and performance of data pipelines and analytical models at scale.
  
• Drive root cause analysis and resolution of complex data issues across upstream and downstream systems.
  

Enablement of Advanced Analytics & BI

• Partner with data scientists, analysts, and BI teams to enable advanced analytics and AI use cases.
  
• Design data models that support scalability, performance, and flexibility for diverse analytical needs.
  
• Champion self-service analytics by creating well-structured, accessible, and performant datasets.
  

Leadership, Governance & Standards

• Define and implement analytics engineering standards, governance frameworks, and best practices.
  
• Lead documentation initiatives for data models, definitions, and lineage.
  
• Ensure compliance with data governance, security, and regulatory requirements.
  

Stakeholder Engagement & Strategy

• Collaborate with business and technical stakeholders to align data models with strategic objectives.
  
• Translate complex business requirements into scalable and maintainable data solutions.
  
• Contribute to data platform strategy, roadmap, and tooling decisions.
  

Mentorship & Team Development

• Mentor and guide junior and mid-level analytics engineers.
  
• Conduct code reviews and provide technical oversight.
  
• Foster a culture of continuous learning, collaboration, and excellence.
  

Role Competencies

Technical

• Expert-level SQL and advanced data modelling expertise.
  
• Deep experience with modern data stack tools and architectures.
  
• Strong understanding of data pipelines, orchestration, and dependencies.
  
• Familiarity with software engineering best practices in data (CI/CD, testing, modular design).
  

Analytical & Problem-Solving

• Ability to design scalable solutions to complex data challenges.
  
• Strong critical thinking and data validation skills.
  
• Proactively identifies risks, inefficiencies, and improvement opportunities.
  

Communication & Collaboration

• Strong stakeholder management and communication skills.
  
• Ability to translate technical concepts into business-friendly insights.
  
• Produces high-quality documentation and promotes knowledge sharing.
  

Leadership & Influence

• Demonstrates technical leadership and decision-making capability.
  
• Influences data strategy and promotes best practices across teams.
  
• Builds alignment across cross-functional teams.
  

Adaptability and Agility       

• Thrives in dynamic, fast-paced environments.
  
• Continuously evolves tools, practices, and skillsets.
  
• Drives innovation in analytics engineering approaches.