• Lead complex data transformation initiatives, improving data quality, performance, and scalability, Identifying gaps, risks, and opportunities in the analytics stack and drive improvements. • Champion analytics best practices, data visualization standards, and innovative measurement methodologies. Furthering the advancement of Ai use and adoption, automation across analytics, and partnering with Internal Teams for best practice and streamlining where applicable. • Support advanced analytics use cases such as experimentation, attribution, and forecasting. • Enable self-service analytics by delivering well-modelled, well-documented datasets. • Trusted advisor to Planning, Activation, Analytics leadership and external stakeholders to define KPIs, measurement frameworks, and data strategies. • Strong English language and presentation skills, the Senior Data Analyst will proactively lead analytics client calls. Can diagnose problems, understand the challenge, and set clear objectives, KPIs, and provide recommendations to meet requirements. • Serve as a technical leader and point of escalation within the Analytics Team, mentoring and advising other Analytics Team members. • Confidently turn insights into strategy, and able to provide strategic counsel and recommendations to clients of all sizes including enterprise clients / large budget projects. Effectively taking a brief and delivering to deadline.

- You will work on Data Modelling and Analytics Engineering project to improve, enrich and maintain our data models and Analytics pipelines. Those projects will be in close collaboration with the head of Data & Analytics as the main stakeholder. The objective of those project is to further enable data analytics and scientist from the team to bring more impact to the business use cases they are working on in collaboration with business teams. - You will be responsible for contributing effectively to our code base: building, testing, reviewing and maintaining solid analytics pipelines using SQL and dbt. Help managing TechDebt and improving engineering practices and the project's architecture are also important responsibilities for this role. Solid methodical testing is key here to strengthen Data Observability. - You are expected to gradually own some aspects of the team's responsibilities (some parts of the code base, , have a strong saying in how the analytics project's architecture should evolve, contribute to team's evolution and continuous growth, help data analysts and scientist improve and strengthen their engineering skills ...) - You will be expected to speak up your mind and contribute proactively and effectively to improving the team's practices, cohesion, impact and mission - You are expected to be highly autonomous and show a sense of ownership and ability to effectively manage your own projects and stakeholders. This should be fulfilled with minimum guidance from the Head of Data & Analytics - You will help mentoring more junior members and sharing knowledge and practices within the team to level up everyone's skills - You are expected to contribute effectively to our functional documentation in a way that is clear, concise and useful for future collaborators and readers

• Provide Strategic Reporting to Leadership • Serve as the primary point of contact and accountable owner for all recurring and ad hoc operational and carrier/client reporting deliverables • Ensure data used for strategic decision-making is accurate, validated, timely, and actionable • Develop and enforce stringent standards for reporting requirements • Serve as a technical subject matter expert for operational databases, reporting datamarts, and related interfaces • Guide the collection, synthesis, and interpretation of operational data to identify trends and productivity opportunities • Direct and oversee project execution across the analytics team • Manage Data Analytics associates, including workload assignment and performance management • Establish standards for documentation, coding, testing, and system support.

• Teaching our learning content to groups of APAC-based adult learners via virtual workshops • Honing your skills as an educator and building your knowledge of andragogy and teaching theory • Working with the Learner Success Coaches (LSC) to ensure learners progress through the programme • Workshop Preparation: Reviewing exercises and other learning materials before a session • Learning about your learners and the businesses they work for • Familiarising yourself with the course structure and content, proactively filling gaps in your knowledge • Self-assessing your skills and taking up suitable training and professional development opportunities • Workshop delivery: Delivering a 10/10 learning experience in each workshop • Communicating complex data science concepts and techniques with energy, focus and patience • Engaging and captivating the attention of a virtual room of busy working professionals • Facilitating the learning journey for your audience, and leaving no-one behind whilst also providing stimulation for advanced learners • Enabling an inclusive, collaborative, and respectful learning environment