Senior Analytics Engineer

Job Description

Job Description

About the Company

We are building the "TikTok of interactive mini-apps"—a 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 You'll 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 metrics—including 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 experience—specifically 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.