全职 Data Analytics Lead 工作, 薪水, MR DIY International Selangor 公司招聘中 - Ricebowl

Job Summary

The Data Analytics Lead turns MR DIY's data into decisions. Where the Data Engineering Lead builds and governs the pipelines and infrastructure, this role owns the analytics layer on top of it — business intelligence, reporting, advanced analytics, and self-service enablement — ensuring that leaders, business units, and store operations have trusted, timely, and actionable insight.

This is a hands-on leadership role: defining the analytics agenda, partnering closely with Data Engineering and the wider Digital Transformation team, and growing an analytics function that scales across MR DIY's multi-country, multi-brand retail footprint.

Key Responsibilities

Analytics Strategy & Roadmap

• Define and own the analytics roadmap, aligning it with MR DIY's business objectives and the broader digital transformation strategy.
• Identify high-value analytics opportunities across merchandising, supply chain, store operations, finance, and customer/loyalty domains, and prioritise based on business impact.
• Translate ambiguous business questions into structured analytics problems with clear success measures.
  
  

Business Intelligence & Reporting

• Own the Business Intelligence and reporting ecosystem — define standards for dashboards, metrics, and KPI definitions so the business works from a single source of truth.
• Establish and govern a consistent metrics layer (semantic definitions, naming, calculation logic) to prevent conflicting numbers across teams.
• Oversee development and maintenance of executive, operational, and self-service dashboards.
  
  

Advanced & Decision Analytics

• Lead delivery of descriptive, diagnostic, and where appropriate predictive analytics (e.g. demand forecasting inputs, inventory and assortment insight, store performance analysis).
• Partner with data science / AI counterparts to operationalise models and ensure analytical outputs are interpretable and adopted by the business.
• Champion experimentation and measurement (e.g. A/B testing, cohort analysis) to support evidence-based decisions.
  
  

Data Quality & Governance (Consumption Side)

• Define data quality, accuracy, and freshness expectations for analytics outputs, and work with the Data Engineering Lead to enforce them upstream.
• Establish governance over metric definitions, dashboard certification, and access controls in collaboration with data governance standards.
• Ensure analytics practices comply with relevant data privacy and regulatory standards across operating markets.
  
  

Self-Service Enablement & Data Literacy

• Build and promote a self-service analytics capability so business teams can answer their own routine questions safely.
• Drive data literacy across the organisation through training, documentation, and enablement.
• Curate and maintain trusted, well-documented datasets and reporting templates for business users.
  
  

Stakeholder Engagement

• Act as a bridge between business units and the data function — gathering requirements, managing expectations, and prioritising competing demands.
• Communicate insights and recommendations clearly to senior leadership, tailoring depth to the audience.
• Keep stakeholders informed on roadmap progress, adoption, and the business value delivered by analytics.
  
  

Team Leadership & Capability Building

• Build, mentor, and grow the analytics team (analysts, BI developers, analytics engineers as applicable).
• Establish best practices, ways of working, and quality standards within the team.
• Foster a collaborative, performance-driven culture across the team and the wider organisation.
  
  

Performance, Operations & Vendor Management

• Monitor adoption, usage, and performance of analytics and BI platforms; track KPIs to identify areas for improvement.
• Manage key analytics/BI vendors and tooling — ensuring SLAs, performance, and contract obligations are met.
• FinOps: own the analytics tooling budget (build vs run), evaluate the financial impact of licences and new investments, and ensure cost efficiency and scalability.
• Support internal and external audits with required documentation and evidence.
  
  

Job Requirements

• Bachelor's / Master's degree in Data Science, Statistics, Computer Science, Business Analytics, or a related field.
• 8+ years of relevant experience in data analytics / business intelligence, with at least 3 years in a leadership or team-lead capacity (adjust to your hiring bar).
• Strong proficiency with BI / visualisation tools (e.g. Power BI, Tableau, Looker, Holistics) and SQL; familiarity with cloud data platforms (e.g. AWS, Azure, GCP).
• Proven track record of delivering analytics that influenced business decisions and delivered measurable value.
• Solid understanding of data modelling, metric/semantic layers, and data quality principles.
• Experience working alongside data engineering and data science functions in a modern data environment.
• Excellent communication, storytelling-with-data, and stakeholder management skills.
• Enterprise, Retail, e-commerce, or large-scale consumer business experience is an advantage
  
  

At MR DIY International, we're more than a global home improvement brand, we're a catalyst for ambitious talent ready to grow beyond borders.

With over 5,000+ stores across 15 countries globally, we offer unmatched international exposure to those looking to build a meaningful, global career.

From retail operations and merchandising to strategy, tech, and supply chain. Your work here shapes how millions of customers shop every day.

Our Culture Is Built On GIVE

Growth: Rapid development opportunities at every stage of your journey.

International Exposure: Cross-border collaborations and secondments.

Value Creation: Real ownership, real impact.

Empowerment: A culture that trusts you to lead and innovate.

If you're driven, adaptable, and eager to be part of a high-impact team, MR DIY International is where you build, lead, and thrive.