Responsibilities

Own and evolve Sekai’s data warehouse architecture, with BigQuery as the core analytical platform.
Build reliable data pipelines, data models, and intermediate tables for product, growth, and company-level analysis.
Manage product analytics integrations across PostHog, Amplitude, and internal data sources.
Partner with Product and Backend Engineering on event instrumentation, tracking quality, and user journey visibility.
Create trusted source-of-truth datasets that help teams make faster and more consistent decisions.
Build clean reporting workflows, dashboards, and self-serve analytics foundations for internal teams.
Improve data quality, freshness, documentation, observability, and access patterns.
Optimize BigQuery cost and performance through thoughtful architecture, table design, and query practices.

Requirements

5+ years of professional experience in data engineering, data platform engineering, or a closely related role.
Strong hands-on experience with SQL, Python, and large-scale analytical data systems.
Deep experience with BigQuery, including warehouse design, query optimization, and cost-aware data modeling.
Experience working with product analytics tools such as PostHog, Amplitude, or similar platforms.
Strong understanding of consumer app analytics, including funnels, retention, cohorts, activation, engagement, and user journeys.
Ability to understand business and product needs, then translate them into scalable data architecture.
Comfortable working with backend engineers on tracking, data contracts, APIs, logs, and production data flows.
High ownership mindset; able to operate independently in a fast-moving startup environment.

Experience as an early data hire or primary owner of a startup data platform.
Experience building or rebuilding a data warehouse from 0 to 1.
Background in consumer apps, social products, UGC platforms, creator tools, gaming, or mobile-first products.
Backend engineering experience or strong familiarity with backend systems.
Experience building AI-ready data workflows, internal data tools, or governed self-serve analytics systems.
Strong side projects, open-source work, or independently built data products.