Data Analyst (Product, FinTech)

Location: Remote

Company

hirify.global develops software services in the FinTech space, focused on simpler and faster ways to transact and manage investments.

What you will do

• Analyse user behaviour and product journeys across web and mobile applications, including funnels, activation, engagement, retention, monetisation, and feature adoption.
• Identify drop-offs, friction points, behavioural patterns, and growth opportunities, then translate findings into product hypotheses.
• Design and analyse A/B tests and other experiments, including success metrics, guardrails, segmentation, sample-size considerations, and statistical interpretation.
• Define product KPIs and metric trees, and build dashboards and analytical views for Product teams.
• Build user and behavioural segmentations and compare cohorts to understand which product experiences work for different users.
• Help build product data marts and single-source-of-truth datasets while improving event tracking, instrumentation, documentation, and metric consistency.

Requirements

• 3+ years of experience in data analytics.
• Strong SQL and Python skills, with hands-on experience working with large datasets.
• Strong understanding of product analytics concepts, including 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.
• Advanced Russian and English, plus clear communication and cross-functional collaboration skills.

Nice to have

• Experience building analytical data marts or working with dbt-style analytics engineering workflows.
• Experience owning event tracking across web and mobile applications.
• Understanding of marketing analytics, acquisition, and attribution.

Culture & Benefits

• Work in a global team of engineers, data scientists, marketeers, and financial experts.
• Collaborate closely with Product, Commercial, Engineering, and Data Engineering teams.
• Use a modern analytics stack including Snowflake, ClickHouse, MySQL, BigQuery, Tableau, HEX, Amplitude, Python, and Jupyter Notebook.