(Senior) Product Data Analyst

Turn product data into better decisions, experiences, and measurable customer impact.

Smartly is an AI-powered advertising platform uniting creative and media workflows. We are looking for a Product Data Analyst who helps product teams understand how customers use the platform, choose investments, and measure whether launches create value.

About the role

Reporting to the Manager, Data Platform, you will be a hands-on contributor in the Data Analytics squad, partnering closely with Product, Design, Engineering, and Data Engineering across the company.

This role combines analytics engineering with product analytics. You will write code, build production-grade models and trusted data products, then use them to answer product questions and shape decisions for teams across Smartly. Dashboards are a delivery surface, not the goal.

Smartly moves fast. You will turn ambiguity into a plan, deliver in valuable increments, make sound technical choices, surface blockers early, and drive work through adoption. This is not a coordination or reporting-production role.

What you will own

Build production-quality dbt models in BigQuery, from source-aligned layers to reusable product marts and metric foundations.

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.

What success looks like

Within three months, you understand the product and architecture, contribute reviewed code regularly, and ship a trusted model or improvement.

Within six months, you own a product area’s analytics foundations: tested, documented models actively used in decisions and trusted by product teams.

You replace recurring manual analysis and reporting with reusable models, reliable metrics, and effective self-service.

Your pace shows in completed outcomes: you move without perfect information while maintaining quality and alignment.

What we are looking for

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: you 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.

Nice to have

Experience with orchestration, semantic layers, data contracts, lineage, observability, or near-real-time data.

Experience with experimentation, causal inference, or advanced statistics.

Experience in B2B SaaS, adtech, or complex workflow products.

Experience with Tableau, Redash, or similar BI tools.

How we work

Join an international, fast-moving team where Product, Engineering, Design, and Analytics work together with clear personal accountability. We value ownership, simplicity, learning, direct feedback, and sustainable delivery. This Helsinki-based role follows Smartly’s hybrid practices, with regular in-person collaboration.

What we offer

Product work with visible customer and business impact.

Autonomy, supportive peers, and room to grow your craft.

A global, inclusive team built on trust and open feedback.

Competitive local compensation, benefits, and wellbeing support.