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.

Experience
10-14 yrs