Senior Analytics EngineerSuccess Matcher Recruitment • San Francisco, California, United States
Senior Analytics Engineer
Success Matcher Recruitment
- San Francisco, California, United States
- San Francisco, California, United States
Über
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
Technical Screen: A deep dive into data modeling, SQL, and dbt expertise, focused on past projects where you transformed raw events into clean datasets.
Final Round: A conversation with leadership evaluating product analytics thinking, consumer metric understanding, and founding team culture fit.
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Sprachkenntnisse
- English
Hinweis für Nutzer
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