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.

Requirements

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

Benefits

Perks and benefits:

- Competitive salary and performance-based bonuses.

- Comprehensive insurance plans.

- Collaborative and supportive work environment

- Chance to learn and grow with a talented team.

- A positive and fun work environment.