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 3–4 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

• 6–9 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, Monday.com, 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