Analytics Engineer

What we do:

GMG is a global well-being company retailing, distributing and manufacturing a portfolio of leading international and home-grown brands across sport, everyday goods, health and beauty, properties and logistics sectors. Under the ownership and management of the Baker family for over 45 years, GMG is a valued partner of choice for the world's most successful and respected brands in the well-being sector. Working across the Middle East, North Africa, and Asia, GMG has introduced more than 120 brands across 12 countries. These include notable home-grown brands such as Sun & Sand Sports, Dropkick, Supercare Pharmacy, Farm Fresh, Klassic, and international brands like Nike, Columbia, Converse, Timberland, Vans, Mama Sita's, and McCain.

What will you do:

We are hiring an Analytics Engineer to work closely with the Data Scientist leading analytics for a specific Line of Business (LOB) and partner with LOB senior leadership to deliver trusted data products and decision-ready insights. You will build and maintain data marts, semantic layers, and foundational reporting, conduct deep-dive analysis, and support structured ML solutions in collaboration with the Data Scientist - bridging business needs and scalable data models.

Role Summary:

- Build and maintain curated data marts and a consistent semantic layer for the LOB.

- Develop core reporting and dashboards; deliver analysis and insight narratives for leadership.

- Translate ambiguous business needs into well-defined analytical problems, hypotheses, and measurement plans.

- Support ML-ready datasets and structured ML solutions in partnership with the Data Scientist.

Responsibilities:

Data marts & semantic modeling:

- Design and implement curated data marts for the LOB (facts/dimensions, metric definitions, governed datasets).

- Define and maintain a semantic layer that standardizes business KPIs, dimensions, and calculation logic.

- Ensure models are reusable, performant, and maintainable (clear grain, lineage, documentation).

Reporting & self-serve analytics enablement:

- Build foundational reports and dashboards that support leadership reviews and operational decision-making.

- Create single source of truth KPI views and ensure metric consistency across stakeholders.

- Improve data discoverability with documentation, data dictionaries, and usage guidance.

Analysis, Synthesis & Insights:

- Conduct exploratory analysis and deep dives to answer key business questions, identify drivers, and recommend actions.

- Formulate hypotheses, define success metrics, and quantify impact (uplift, cost, productivity, risk).

- Communicate insights through crisp storytelling: problem insight recommendation expected impact.

Collaboration with Data Scientist on ML solutions:

- Support structured ML initiatives by producing clean, point-in-time correct feature datasets, labels, and evaluation slices.

- Implement operational reporting for ML solutions (performance tracking, adoption metrics, drift indicators where needed).

- Assist in packaging outputs into business workflows (e.g., decision tables, score exports, prioritized lists).

Stakeholder partnership & delivery execution:

- Work closely with the LOB Data Scientist to align on priorities, delivery roadmap, and stakeholder expectations.

- Engage senior leadership with strong presentation skills, influencing through data and recommendations.

- Navigate complexity and ambiguity; drive clarity, alignment, and delivery momentum.

How does success look like:

- LOB leaders trust the data: consistent KPIs, clean definitions, and reliable reporting cadence.

- Key data marts and semantic models are in place and actively used for decision-making.

- You independently deliver high-quality analyses that translate into clear actions and measurable outcomes.

- ML initiatives accelerate because feature datasets and analytical foundations are robust and reusable.

- Stakeholders experience faster turnaround and fewer debates about which number is correct.

Technical Competencies:

- Strong experience building analytics data products: marts, semantic layers, and decision-ready reporting.

- Advanced SQL and solid data modeling skills (facts/dims, grain clarity, metric consistency).

- Proven ability to translate ambiguous business needs into structured analytical problems and hypotheses.

- Excellent communication, stakeholder management, and presentation skills- able to influence senior stakeholders.

- Comfortable working in complex environments with multiple stakeholders and evolving priorities.

Required technical skills:

Mandatory:

- Advanced SQL (window functions, CTEs, performance tuning, incremental patterns).

- Data modeling for analytics: dimensional modeling, metric definitions, semantic consistency.

- Strong analytical foundations: hypothesis-driven analysis, cohorting, funnel analysis, KPI decomposition.

- Data quality mindset: checks for freshness, completeness, anomalies; documentation and lineage awareness.

- Practical proficiency with BI tools and building dashboards/reports (tool-agnostic).

- Version control and collaborative development practices (Git, code reviews).

Good to have:

- Experience with transformation frameworks (e.g., dbt) and modern lakehouse/warehouse patterns.

- Familiarity with experimentation design and basic causal measurement approaches.

- Experience supporting ML solutions: feature engineering support, point-in-time datasets, model monitoring dashboards.

- Python for analysis/automation (not mandatory, but beneficial).

Qualification & Experience:

- Graduation or Masters in Statistics, Mathematics, Computer Science or equivalent

- 5+ years of hands-on Analytics engineering/Data Engineering experience