Data Analyst – Product
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Job Description
Data Analyst improving product analytics for Finyard’s global FinTech software services. Designing experiments, analyzing user behavior, and building scalable metrics, dashboards, and data foundations.
Responsibilities:
- Partner closely with Product, Commercial, and Engineering teams to understand user behaviour
- Evaluate and generate product hypotheses
- Design and analyse experiments
- Improve key business and product metrics
- Build scalable analytics foundations including metrics, data marts, dashboards, and alerting
- Analyse user behaviour and product journeys across web and mobile applications, including funnels, activation, engagement, retention, monetisation, and feature adoption
- Identify drop-offs, behavioural patterns, friction points, and growth opportunities and translate them into actionable product hypotheses
- Evaluate the impact of product launches and changes and explain what changed, why it changed, and for whom
- Formulate and prioritise hypotheses and define measurable success criteria
- Design and analyse A/B tests and other experiments, including primary metrics, guardrails, sample-size considerations, segmentation, and statistical interpretation
- Translate experiment results into clear product decisions and next steps
- Define and maintain product KPIs and metric trees with consistent definitions across teams
- Develop dashboards and analytical views for Product teams
- Build user and behavioural segmentations based on lifecycle stage, product usage, engagement, monetisation, and other characteristics
- Analyse differences between user cohorts and segments
- Formulate analytical and business requirements and participate in building product data marts and single-source-of-truth datasets
- Partner with Engineering and Data Engineering on event tracking and instrumentation
- Improve the product metrics system, including definitions, documentation, ownership, and consistency
Requirements:
- 3+ years of experience in data analytics (fintech/product experience is a strong plus)
- Strong SQL and Python skills and hands-on experience working with large datasets
- Strong understanding of product analytics concepts: funnels, conversion, retention, cohorts, engagement, monetisation, and segmentation
- Hands-on experience with A/B testing and experiment analysis, including statistical significance, confidence intervals, guardrail metrics, and common sources of bias
- Experience with product analytics tooling and event-based tracking
- Ability to communicate insights clearly and collaborate with cross-functional stakeholders
- Advanced Russian and English
- Nice-to-have: Experience building analytical data marts or working with dbt-style analytics engineering workflows
- Nice-to-have: Experience owning event tracking across web and mobile applications
- Nice-to-have: Understanding of marketing analytics, acquisition, and attribution
- Technological stack includes Snowflake, ClickHouse, MySQL, BigQuery, Tableau, HEX, Amplitude, Python (pandas, numpy, scikit-learn), and Jupyter Notebook
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