Senior Data Analyst

Role Summary
We are looking for a Senior Data Analyst with strong technical depth and product sense to help us measure what matters, improve data quality, and turn platform data into clear reporting, insights, and growth opportunities. You will own analytics across key product and commercial areas, lead instrumentation and tracking practices, and partner closely with Product, Engineering, Growth, and Business teams in a high-scale digital platform environment.

Key Responsibilities
• Own end-to-end product analytics: define KPIs, build reporting, generate insights, and recommend opportunities that translate into shipped improvements and measurable impact.
• Build scalable, trusted dashboards and recurring reports for leadership and squads.
• Lead measurement planning for new features and initiatives, including defining success metrics, baselines, and monitoring.
• Drive analytics deep-dives (funnels, cohorts, retention, segmentation, LTV, churn, attribution as relevant) to identify growth levers and friction points.
• Design and analyze experiments (A/B testing and incrementality where applicable), and clearly translate outcomes into decisions and next steps.
• Own data tracking and instrumentation standards:
Create and maintain tracking plans, event taxonomies, and analytics specifications.
Partner with Engineering to implement event instrumentation across web and mobile (client-side and server-side where relevant).
Validate tracking via QA, data reconciliation, and anomaly detection to ensure accuracy and completeness.
• Improve data foundations in collaboration with Data Engineering:
Contribute to data modeling and transformation workflows (dbt or equivalent).
Help define and maintain semantic layers / metric definitions to ensure consistency.
Monitor data quality and reliability (freshness, completeness, accuracy).
• Produce clear, executive-ready narratives and recommendations for technical and non-technical stakeholders.

What Success Looks Like

• Teams trust the metrics and dashboards as the source of truth, with consistent definitions and minimal data disputes.

• Tracking and instrumentation are reliable, documented, and scalable across product surfaces.

• Insights consistently influence prioritization and lead to measurable improvements in activation, retention, conversion, and revenue.

• Experimentation becomes faster, more rigorous, and more decision-oriented.

Required Qualifications
• 5+ years of experience in product analytics / data analytics within a high-scale digital platform (marketplace, SaaS, consumer app, fintech, martech, or similar).
• Expert SQL and strong experience working with large, complex datasets.
• Strong proficiency in analytics and reporting tools (Looker, Power BI, Tableau, Metabase, Superset, or similar).
• Hands-on experience with tracking and data instrumentation:
Event-based analytics, tracking plans, and event taxonomies.
Collaboration with engineers on implementation and QA.
Familiarity with tools like Segment, RudderStack, Snowplow, mParticle, Amplitude, Mixpanel, GA4 (or equivalents).
• Solid statistical foundations: experimentation, inference, cohort analysis, and measurement design.
• Strong product sense with the ability to frame ambiguous problems, define metrics, and influence roadmaps with evidence.
• Strong communication and stakeholder management, including leadership-level storytelling.
Preferred Qualifications (Bonus)
• Proficiency in Python (or R) for analysis, automation, and advanced modeling.
• Experience with modern data stacks (BigQuery/Snowflake/Redshift, dbt, Airflow/Dagster).
• Experience with AI-related analytics or applied AI use cases, such as:
Building evaluation frameworks for model performance (accuracy, drift, bias, latency).
Experimenting with recommendation/ranking systems, predictive models, or LLM-powered features.
Creating monitoring and reporting for AI features in production.
• Familiarity with privacy-aware tracking and consent frameworks (where relevant).

Core Skills & Behaviors

• High ownership and practical prioritization, with a bias toward action and impact.

• Strong analytical rigor and attention to data quality details.

• Collaborative partner who can align Product and Engineering on measurement and tracking.

• Clear communicator who can simplify complexity and drive decisions.