A day in your life might include (what you will be doing/ key responsibilities)
Key Responsibilities
1. Team Leadership & People Management• Manage a team of 4
Data Analysts of mixed seniority: set goals, allocate work across initiatives, and review output quality.
• Own hiring, onboarding, and performance management for the team, including regular 1:1s, feedback, and career development plans.
• Build team rituals, ways of working, and coding/analysis standards that scale as headcount grows.
2. Product Analytics Strategy & Roadmap• Define and prioritize the analytics roadmap across product verticals in partnership with Product leadership.
• Decide what the team should - and should not - spend time on, balancing ad-hoc stakeholder requests against strategic analysis.
• Establish the core metrics framework (business outcome, customer guardrail, and execution-quality tiers) for major product initiatives.
• Own the analytics tooling, event tracking, and data-quality agenda for the pod (data warehouse, dbt models, dashboards), in coordination with Data Engineering.
3. Actionable Insights & Stakeholder Management• Business and product fluency: Understand target customers, their needs and pain points; connect data insight to product strategy and contribute directly to roadmap planning.
• Insight delivery: Use data storytelling - not just reporting - to influence stakeholders and drive product decisions.
• Stakeholder alignment: Manage stakeholder expectations - set realistic timelines and proactively communicate challenges or roadblocks before they become escalations.
• Go-live / GTM: Work with Marketing, Sales, and Product to keep go-to-market activities aligned and effective; report on GTM performance to executive leadership.
4. Technical Oversight & Quality• Review and quality-check the team's analysis for rigor, especially for high-stakes or leadership-facing work.
• Stay hands-on enough to unblock the team on complex technical problems and to credibly evaluate their work.
• Set technical best practices and documentation standards across the pod, ensuring consistency as the team grows.
Must-Have
• Bachelor's degree in Statistics, Computer Science, Data Science, Economics, or another quantitative field; Master & MBA a plus.
• 6-8+ years of data analysis experience, including at least 2 years directly managing an analyst team (hiring, performance reviews, career development).
• Strong SQL and/or Python (pandas); comfortable reviewing others; technical work, not only producing your own.
• Track record of partnering with Product or
Business leadership to shape an analytics agenda, not just execute requests.
• Demonstrated experience presenting to and influencing senior/executive stakeholders.
• Experience in fintech, banking, or a fast-scaling tech/e-commerce environment.
Nice-to-Have
• Experience building or scaling an analytics team or function from a smaller base.
• Familiarity with modern data warehousing and transformation tools (BigQuery, Redshift, Snowflake, dbt, etc.).
• Exposure to lending, payments, or digital banking data and regulatory context.
• Experience with product/CRM analytics platforms (Amplitude, MoEngage, AppsFlyer, Firebase, etc.).