Product Data Analyst (L3) Job at Stytch

Job Responsibility

End-to-End Analytics: Own complex projects from scoping ambiguous problems to delivering actionable, data-driven insights
Product Strategy: Partner with PMs to define metrics, build reporting dashboards, and inject data into product planning
Technical Execution: Apply SQL and visualization tools (Looker, Tableau) to translate complex data into clear stakeholder recommendations
Data Standardisation: Maintain and document metrics, datasets, and models using Confluence and Datahub to ensure a single source of truth
Project Management: Balance multiple concurrent initiatives with clear prioritization and proactive status updates
Quality Assurance: Maintain rigorous data quality standards through robust validation and transparent quality control
AI-Driven Efficiency: Integrate modern AI tools into daily workflows to speed up SQL coding, analysis, and documentation

4+ years of experience in quantitative analysis, business intelligence, or data analytics, with a proven track record of converting complex, ambiguous data into actionable business results
4+ years of hands-on experience writing, optimizing, and performing quality assurance (QA) in SQL or relational database languages on a regular basis
Proficiency with modern BI tools, with a strong preference for Tableau and Looker (LookML)
Strong analytical mindset with the ability to decompose vague problem statements into clear, executable data strategies
Demonstrated ability to independently manage deliverables from initial requirement gathering through to stakeholder delivery with minimal guidance
Exceptional organizational skills with a track record of juggling multiple competing priorities and driving tasks to completion with reliable follow-through
Strong written and verbal communication skills with the ability to translate analytics insights into clear narratives and action items that influence stakeholder decisions

Experience leveraging LLMs/AI tools to accelerate analytics workflows, combined with a strong curiosity to continuously upskill as technology evolves
Familiarity with data warehousing concepts, ETL processes, and distributed computing frameworks such as AWS Glue, Spark, DBT, Athena
Proficiency in Python (Pandas, NumPy, Scikit-Learn) or R for statistical modeling, regression analysis, hypothesis testing, and causal inference