Smartly is seeking a Product Data Analyst to join the Data Analytics squad, working closely with Product, Design, Engineering, and Data Engineering teams. The role involves building production-grade dbt models in BigQuery, designing data tables, writing maintainable code, and creating durable data products to support product decisions. Responsibilities include partnering on data instrumentation and quality, analyzing user journeys and experiments, and delivering decision-ready dashboards. Candidates should have advanced SQL and BigQuery skills, experience with dbt, Python coding ability, strong data modelling judgment, and product analytics expertise. The role requires strong delivery skills, clear communication, and curiosity about digital advertising. This is a full-time, mid-level position based in Helsinki with hybrid work practices. Benefits include impactful product work, autonomy, a global inclusive team, and competitive local compensation.
What you'll do
Build production-quality dbt models in BigQuery, from source-aligned layers to reusable product marts and metric foundations
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Design tables deliberately, defining grain, keys, joins, data types, null handling, history, and performance before implementation
Write clean, maintainable code using version control, pull requests, automated tests, documentation, and peer review
Turn recurring questions and one-off reports into durable models, shared definitions, and self-service data products
Partner with Product Managers and Engineers on instrumentation, event schemas, data contracts, validation, and quality monitoring
Own domain data quality by tracing discrepancies, lineage, and join coverage; fix root causes before users find them and expose reliability
Analyse journeys, funnels, cohorts, retention, adoption, experiments, and commercial outcomes to recommend next steps
Build decision-ready dashboards when appropriate, keeping trusted modelling and metric logic beneath the visualisation
Drive work end to end: clarify outcomes, scope pragmatically, ship iteratively, communicate directly, and ensure adoption
Use AI tools to increase speed while protecting confidential data, reviewing generated code, and verifying conclusions
Requirements
Advanced SQL and strong experience with BigQuery or another cloud warehouse, including complex transformations, window functions, nested data, type conversion, and performance trade-offs
Strong modelling judgement across warehouse and analytics use cases: grain, cardinality, dimensional models, deduplication, historical data, and the metric risks of poor joins or types
Hands-on dbt or similar experience, including modular design, testing, documentation, lineage, version control, and production deployment workflows
Ability to code beyond dashboards—for example, using Python to inspect data, automate workflows, use APIs, or build analytical tools
An engineering mindset focused on correctness, maintainability, observability, reproducibility, and solutions others can safely extend
Practical product-analytics judgement across funnels, cohorts, retention, adoption, segmentation, and product-impact measurement
Strong delivery instinct: navigate ambiguity and shifting priorities, decide with available evidence, and unblock progress
Clear stakeholder communication: translate business needs into technical designs, explain trade-offs, challenge assumptions, and recommend action
Curiosity about digital advertising and motivation to learn enough product and customer context to model data correctly
Tech stack
SQLBigQuerydbtPythonTableauRedash
Benefits
Product work with visible customer and business impactAutonomy, supportive peers, and room to grow your craftA global, inclusive team built on trust and open feedbackCompetitive local compensation, benefits, and wellbeing support
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