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.