Senior Analytics Engineer

About the Company

We are building the TikTok of interactive mini-appsa high-growth consumer social platform where users scroll through a feed of playable bite-sized experiences and create their own simply by describing what they want. Our AI-powered creation flow turns natural language into shareable interactive content instantly.

Backed by top-tier VCs including a16z Khosla and Mayfield we have raised $30M grown to over 1 million monthly active users (MAUs) and are scaling rapidly to become a major consumer platform.

Why You Should Join

• Founding Analytics Role: You will be data hire #1 giving you total ownership over our entire data layer from scratch. You define how we understand user behavior creator dynamics and viral growth loops.
• Massive Scale Early Stage: Work with complex high-volume clickstream and product event logs for 1M active users. Your metrics and models will directly drive company strategy and product roadmaps.
• Full Technical Autonomy: You own the architecture decisions data culture and tooling choices (BigQuery/Snowflake dbt) from day one with zero legacy technical debt or red tape.
• Engaged Leadership: You will report directly to a highly communicative engineering and product leadership team that averages a 4-hour response time and moves fast with the right candidates.

What Youll Be Doing

• Design and maintain core analytics data models transforming messy high-volume raw events and app logs into clean trusted analysis-ready tables.
• Define model and operationalize company-wide metricsincluding DAU/MAU ratios user retention curves creator supply health and funnel conversion efficiency.
• Partner directly with product and engineering teams to design and improve event taxonomy clickstream instrumentation and overall data quality across app web and backend.
• Build dashboards and self-serve data products to help growth engineering and leadership teams diagnose product performance independently.
• Establish data quality standards robust testing via dbt clear documentation and freshness checks so the entire organization can trust the numbers.

Role Requirements

Technical Skills & Experience

• 4 years of analytics engineering experiencespecifically focused on building robust data pipelines from raw product events not just front-end BI or dashboarding work.
• Advanced SQL & Data Modeling: Expert-level hands-on experience with cloud data warehouses like BigQuery or Snowflake to build canonical highly reusable datasets.
• dbt Expertise: Highly comfortable building testing documenting and maintaining complex data transformation pipelines using dbt.
• Product Analytics Fluency: A deep understanding of core consumer metrics (DAU retention funnel conversion organic viral loops) and experience setting up environments for experimentation and A/B testing.
• Event Tracking & Instrumentation: Clear understanding of how to define event schemas and collaborate with engineers to instrument clean event tracking into production apps.

Domain & Soft Skills

• Consumer Social or Gaming Exposure: Prior experience working with high-volume behavioral data creator economy dynamics or consumer platform retention mechanics.
• Early-Stage Velocity: Experience in fast-paced startup environments (Seed to Series B). You are highly comfortable with ambiguity shifting priorities and rapid iteration.
• Pragmatic Builder Mindset: You focus on shipping high-impact v1 pipelines quickly and iterating rather than waiting to build flawless over-engineered infrastructure.
• Strong Communication: The ability to explain complex data models or data constraints clearly to product managers designers and business stakeholders.

Profiles We Are Avoiding

• Candidates whose experience is limited strictly to BI tool charting report building or dashboarding without deep data transformation ownership.
• Engineers with an exclusive background in Enterprise/B2B SaaS or fintech metrics who lack experience with high-volume consumer clickstream data.
• Academic or perfectionist mentalities that struggle to ship code quickly in an ambiguous evolving startup environment.

Interview Process

1. Technical Screen: A deep dive into data modeling SQL and dbt expertise focused on past projects where you transformed raw events into clean datasets.
2. Final Round: A conversation with leadership evaluating product analytics thinking consumer metric understanding and founding team culture fit.