Senior Data Analyst

Timo's data volume and stakeholder demand are growing faster than the current Data Analyst team's capacity, driven by expansion across lending (PayLater/Timo Credit), deposits (GoalSave, Term Deposit, CD), and payments. This role creates a senior layer within the team: a technically strong analyst who can own complex, ambiguous problems end-to-end, represent data insights directly to leadership and cross-functional stakeholders, and manage a small pod of junior analysts — freeing the Head of Data to focus on strategic and cross-departmental priorities. A day in your life might include 1. Data Analysis & Technical Delivery • Own end-to-end analysis for high-priority initiatives (e.g., lending, deposits, cards, new user onboarding), from framing the business question to delivering the recommendation. • Write efficient, well-documented SQL and Python (pandas) to extract, clean, and model data from the Group's warehouse (Athena, dbt). • Build and maintain dashboards and self-serve reporting (Holistic Dashboard) that reduce ad-hoc requests to the team. • Design and evaluate experiments (A/B tests), cohort, funnel, and segmentation analyses to support product and growth decisions. • Own, set, and enforce data quality, documentation, and analytical rigor standards and event tracking across the team's outputs. 2. Insight Generation & Stakeholder Communication • Translate complex, technical analysis into clear, decision-ready narratives for non-technical audiences, including senior management. • Present findings and recommendations directly to stakeholders across Product, Business, Risk, and Compliance — including trade-offs, confidence levels, and limitations. • Partner with Product and Business teams to define success metrics and KPIs for new initiatives before launch, not just measure them after. • Proactively surface trends, risks, and opportunities from data, rather than only responding to inbound requests. 3. Team Leadership & Capability Building • Manage and mentor a small pod of junior Data Analysts: allocate work, review quality, and support their technical and career development. • Establish and maintain team best practices — coding standards, QA checklists, and analysis templates — to keep output consistent as the team scales. • Act as the working-level bridge between the Head of Data and junior analysts: unblock issues, manage delivery timelines, and escalate where needed. • Contribute to onboarding new analysts and to the team's broader learning & development plan. How to succeed in this role Must-Have • Bachelor's degree in Statistics, Economics, Computer Science, Data Science, or another quantitative field. • 4–6 years of hands-on data analysis experience, ideally in fintech, banking, or a fast-scaling tech/e-commerce environment. • Strong SQL and proficiency in Python (pandas) for data manipulation and analysis. • Working experience with BI/visualization tools (e.g., Metabase, Redash, Tableau, Power BI, Holistics, Mode, or similar). • Proven track record of presenting data-driven recommendations to senior stakeholders, with strong written and verbal communication. • Some prior experience mentoring, coaching, or informally leading junior analysts. Nice-to-Have • Experience with modern data warehousing and transformation tools (Athena, BigQuery, Redshift, Snowflake, dbt, etc.). • Familiarity with product/CRM analytics platforms (Amplitude, MoEngage, AppsFlyer, Firebase). • Exposure to lending, payments, or digital banking data and regulatory context. • Experience designing and interpreting statistical experiments (A/B testing). Core Competencies • Critical thinking: questions assumptions and framing, not just the request as written — gets to the real business problem. • Executive communication: can simplify complex analysis into a clear recommendation for a non-technical, senior audience. • People management fundamentals: coaching, feedback, prioritization, and accountability for a small team's output. • Stakeholder management: comfortable working across Product, Business, Risk/Compliance, and occasionally external partners. Success Measures - First 2 Months • Independently owns and delivers analysis for at least one major cross-functional initiative. • Junior analyst pod is fully onboarded, with clear work allocation and a visible reduction in the Head of Data's day-to-day oversight load. • At least one recurring manual/ad-hoc reporting need converted into a self-serve dashboard. • Demonstrated ability to present directly to senior stakeholders without needing the Head of Data as a buffer.