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.
