Analytics Lead

About the Role

Key Responsibilities

Analytics & Data Acumen

• Work across structured and unstructured datasets to develop meaningful insights identify trends and support data-driven decision-making.
  
• Write and optimize complex SQL queries in Snowflake to extract transform and validate data; leverage DBT for modelling and pipeline management.
  
• Design and maintain dashboards and reporting frameworks that give stakeholders clear reliable visibility into business performance.
  
• Translate ambiguous business questions into well-scoped analytical problems driving them through to clear and defensible conclusions.
  
• Monitor key data streams and business dashboards flagging and triaging anomalies or irregularities as they arise.
  
• Continuously look for opportunities to improve data quality reporting logic and analytical processes.
  

Client Deliverables & Quality Ownership

• Take end-to-end ownership of analytics deliverables from scoping and analysis through to final output ensuring accuracy timeliness and relevance.
  
• Review and quality-check team members work before it reaches clients or stakeholders upholding high standards of data integrity and presentation.
  
• Establish and enforce quality assurance processes and delivery standards across the team.
  
• Manage workload and priorities across the team to meet SLAs and client commitments consistently.
  
• Act as the escalation point for delivery risks data issues or quality concerns on active engagements.
  

Team Leadership & Development

• Lead coach and develop a team of 34 analysts providing hands-on guidance and constructive feedback on their work.
  
• Foster a culture of curiosity accountability and continuous improvement within the team.
  
• Track team utilization and development needs working with stakeholders to ensure effective allocation of capacity.
  
• Support analysts in building their technical skills and analytical thinking over time.
  

Stakeholder Collaboration & Relationship Building

• Build and maintain strong trusted relationships with internal and external stakeholders acting as a reliable analytics partner not just a delivery resource.
  
• Engage proactively with stakeholders to understand their business context anticipate analytical needs and deliver insights that drive real decisions.
  
• Translate complex data findings into clear narratives tailored to both technical and non-technical audiences.
  
• Serve as the primary point of contact for analytics queries and deliverables ensuring stakeholders are informed and confident in the outputs they receive.
  
• Collaborate with Engineering Product and Operations teams to align on data definitions improve reporting infrastructure and close gaps between data and business needs.
  
• Participate in stakeholder reviews presenting findings and recommendations with clarity and confidence.
  

Required Qualifications

• 69 years of experience spanning both data (engineering operations or pipelines) and analytics (reporting insights BI) disciplines.
  
• Strong SQL skills with hands-on Snowflake experience; familiarity with DBT for data modelling and transformation.
  
• Demonstrated experience owning and delivering analytics outputs to clients or senior stakeholders.
  
• Experience leading or mentoring a small team reviewing analytical work and maintaining delivery quality standards.
  
• Confident communicator able to present data findings manage stakeholder expectations and build relationships at multiple levels.
  
• Strong analytical thinking with a structured approach to problem solving and root cause investigation.
  
• Experience with BI and dashboarding tools (e.g. Tableau Looker Power BI or similar).
  
• Familiarity with project and task management tools such as Jira or equivalent.
  

Preferred Qualifications

• Background in a centralized Analytics Centre of Excellence shared services or managed analytics environment.
  
• Familiarity with data quality frameworks statistical methods or anomaly detection techniques.
  
• Experience in fast-paced client-facing or operationally complex environments.
  
• (Optional) Experience with workflow automation tools or scripting (Python or similar) to streamline repetitive analytical tasks.
  
• (Optional) Exposure to Generative AI or LLM-based tools in analytics workflows reporting automation or insight generation; familiarity with prompt engineering or AI-assisted analysis is a plus