(Senior) Product Data Analyst

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. 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.

Key facts

What you'll do

You will architect and implement analytics foundations in BigQuery, designing deliberate table structures that define grain, keys, joins, data types, null handling, history, and performance before writing a single line of code. You will transform recurring product questions and one-off reports into durable models, shared definitions, and self-service data products that empower teams to explore without constant support. You will partner directly with Product Managers and Engineers to establish instrumentation strategies, data contracts, event schemas, and validation frameworks that ensure quality from the source. You will own domain data quality by tracing discrepancies, lineage, and join coverage, fixing root causes before users encounter issues and continuously exposing reliability metrics. You will analyze customer journeys, funnels, cohorts, retention, adoption patterns, experiments, and commercial outcomes to extract insights and recommend concrete next steps to stakeholders. You will build decision-ready dashboards only when appropriate, ensuring that trusted modelling and metric logic resides beneath the visualization and is not duplicated for every new report. You will drive work end to end, clarifying outcomes, scoping pragmatically, shipping iteratively, communicating directly with stakeholders, and ensuring adoption across product teams. You will leverage AI tools to increase speed while rigorously protecting confidential data, reviewing generated code, and independently verifying every conclusion before it influences product decisions. You will translate complex analytical findings into clear narratives and actionable recommendations that influence product strategy and investment choices.

Requirements

You possess advanced SQL skills with strong experience in BigQuery or another cloud warehouse, including complex transformations, window functions, nested data structures, type conversion nuances, and careful consideration of performance trade-offs in analytical queries. You demonstrate strong modelling judgement across warehouse and analytics use cases, understanding grain, cardinality, dimensional models, deduplication strategies, historical data handling, and the metric risks that arise from poor joins or incompatible data types. You have hands-on dbt or similar experience, including modular design, robust testing strategies, comprehensive documentation, effective lineage tracking, version control discipline, and production deployment workflows that ensure stability. You can code beyond dashboards, for example using Python to inspect data distributions, automate analytical workflows, interact with APIs, or build lightweight analytical tools that extend the platform's capabilities. You maintain an engineering mindset focused on correctness, maintainability, observability, reproducibility, and solutions that other team members can safely extend and maintain over time. You possess practical product-analytics judgement across funnels, cohorts, retention analysis, adoption measurement, segmentation strategies, and accurately quantifying product impact on business outcomes. You have a strong delivery instinct, navigating ambiguity and shifting priorities, making decisions with available evidence, and unblocking progress even when information is incomplete or stakeholders are misaligned. You communicate clearly with stakeholders, translating business needs into technical designs, explaining trade-offs transparently, challenging assumptions when necessary, and recommending well-justified actions based on data. You are deeply curious about digital advertising and motivated to learn enough product and customer context to model data correctly, ensuring that your analytical structures reflect the true realities of user behavior and campaign performance.

Practical notes

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