Specialist-Data Intelligence

Role overview

Data exploration and analysis

• Explore and analyze transactional, sourcing, connectivity, and competitive-intelligence datasets.
• Conduct deep-dive and ad-hoc analyses to identify trends, patterns, and opportunities for revenue and margin improvement.
• Design and evaluate test scenarios to validate hypotheses and maximize internal system value.

Insight generation and communication

• Translate data findings into clear, actionable insights and business recommendations.
• Present analysis to stakeholders using visualizations, compelling narratives, and data storytelling.
• Deliver insights to wider audiences and iterate them to achieve impactful results.

Data products and improvement

• Contribute insights, testing, and assumption validation to the development of Commercial Data Products.
• Help shape KPIs, reporting metrics, and dashboards aligned with business goals.
• Suggest optimization and process-improvement opportunities using data insights.
• Stay current with industry trends and analytics tools.

Stakeholder collaboration

• Collaborate with Sourcing, Revenue Management, Product, Sales, and Marketing teams to understand data needs.
• Act as a data partner and advisor to cross-functional teams and multiple stakeholders.
• Bridge Operations and Data Intelligence to align data standards and methodologies.

Advanced analytics and governance

• Design scalable, automated analytics solutions using SQL, Python or R, and cloud-based data platforms.
• Ensure adherence to enterprise data governance and quality standards with Data Engineering and Governance teams.
• Drive data literacy and analytical best practices across teams.

Profile requirements

• Bachelor’s or Master’s degree in STEM, Business Analytics, or a related field.
• 5+ years of experience as a Data Analyst, preferably in e-commerce, online travel, or digital-first businesses.
• Proficiency in SQL and Python or R, along with advanced Excel.
• Strong experience in commercial, digital product, or data analytics practices.
• Hands-on experience with BI tools such as Tableau, Power BI, Looker, or similar for dashboard development and storytelling.
• Excellent stakeholder management and communication skills with English fluency.
• Experience building and deploying machine learning models for pricing and margin optimization, forecasting, recommendations, mapping, or similar applications.
• Familiarity with big data warehouses such as Google BigQuery, AWS Redshift, or Snowflake.