Director Analytics

Responsibilities:

• Lead the next generation of our Data Mart: Redesign and optimise data models to support large-scale analytics and faster decision-making.
• Define the tools and frameworks for data creation, transformation, and consumption across the company.
• Build unified data views: Integrate behavioural and transactional data into structured, query-optimised flat tables that support diverse analytics use cases.
• Own analytics across mobile, web, marketing, product, and operations data streams, ensuring consistent data definitions and scalability.
• Partner with business, product, marketing, CRM, operations and finance teams to ensure data is democratised and actionable.
• Establish data governance, quality standards, and best practices for the org.
• Drive adoption of self-service analytics and empower teams with the right dashboards, reporting, and insights.

Introduce and scale AI/ML-driven insights:

• Collaborate with product and engineering to enable LLM-powered use cases (e. g., conversational analytics, decision copilots), as well as ML-driven use cases such as personalisation, predictive modelling, and anomaly detection.
• Additionally, build and mentor a cross-functional analytics team spanning business/operations analytics, marketing/CRM analytics, and product/finance analytics.
• Partner with engineering leadership to ensure scalability, reliability, and efficiency of the data infrastructure.

Requirements:

• 8+years in data analytics leadership, with proven success in building data marts and defining enterprise-wide data consumption strategies.
• Strong track record of running analytics across mobile, web, marketing, product, and operations data ecosystems.
• Deep technical expertise in data modelling, flat table structures, and query optimisation for large-scale systems.
• Hands-on experience with both behavioural and transactional data, and integrating them into a single analytics layer.
• Strong knowledge of modern data stack (SQL, ETL/ELT, cloud data warehouses like Snowflake/BigQuery/Redshift, BI tools like Looker/PowerBI/Tableau).
• AI Expertise: Experience working with or enabling AI/ML and LLM-based use cases, including building data pipelines for AI applications, supporting use cases such as process automation and conversational analytics, and partnering with engineering to productionise AI-driven solutions.
• ML Competency: Familiarity with machine learning workflows (data pipelines for ML, feature engineering, model monitoring) and ability to guide data scientists in building predictive/ML models.
• Proven ability to build and scale analytics teams, with experience in functions such as business/operations, marketing/CRM, and product/finance analytics.
• Strong business acumen and ability to translate data into actionable insights for growth, retention, and efficiency.
• Excellent stakeholder management and communication skills, comfortable influencing at the CXO level.