OpenArt is an AI Storytelling and Visual Creation Platform used by millions worldwide. We’re building the next generation of creative tools powered by cutting-edge AI, enabling anyone to create videos, visuals, characters, and stories with unprecedented speed and imagination. We believe the future of creativity is AI-native, and we’re shaping that future.
Own the entire data foundation of a fast-scaling AI company — from raw data to executive metrics.
Build from 0 → 1 — define the architecture that powers product, finance, and company-wide decision making.
High visibility and impact — your work directly informs leadership, product direction, and company strategy.
Founder-led, fast-moving culture — high ownership, low process, high trust.
AI-native company — help define how data supports AI systems, agents, and long-term intelligence.
7–10X revenue growth over the past 2 years — now scaling the data layer to match.
We’re looking for a Founding Data Engineer to build and own OpenArt’s core data platform and source of truth, supporting product, finance, and leadership decision-making.
This is a 0 → 1 role focused on data reliability, modeling, and long-term scalability — not just analytics or dashboarding.
You will define how data is structured, validated, and served across the company — ensuring that key metrics are consistent, trusted, and production-grade.
You’ll work closely with the Head of Data, engineering, and leadership to establish a robust data foundation that scales with the company.
Design and build core data pipelines (e.g., product events, payments, internal systems → BigQuery)
Define and maintain the data warehouse architecture, including schema design, data modeling, and table structure
Establish and own the single source of truth (SOT) for product and business metrics
Build and maintain core data models (user, subscription, revenue, engagement, etc.)
Ensure data consistency across systems (product analytics, billing, internal tools)
Lead data reconciliation efforts (e.g., Stripe vs internal systems vs reporting)
Implement data quality checks, validation, and monitoring systems
Build reliable reporting layers used by leadership and finance (not ad hoc dashboards)
Establish data standards and contracts (event naming, schema governance, tracking consistency)
Partner with engineering to improve instrumentation and data correctness at source
Support downstream teams (analytics, DS) by providing clean, well-documented datasets
Continuously improve data reliability, performance, and cost efficiency
Core Requirements
5+ years of experience in data engineering or analytics engineering
Proven experience building data platforms or warehouses from 0 → 1
Strong SQL and Python — you write clean, production-quality data code
Deep expertise in data modeling, ETL/ELT design, and warehouse architecture
Experience with modern data stack:
BigQuery / Snowflake / Redshift
dbt or similar transformation tools
Workflow orchestration tools (Airflow / Prefect or similar)
Experience working with financial and product data (e.g., payments, subscriptions, usage data)
Strong understanding of data reliability, testing, and validation
Ability to translate business definitions into durable, consistent data models
High ownership — you can define and drive architecture decisions independently
Comfortable operating in ambiguous, fast-moving environments
Nice to Have
Experience building data systems for finance or revenue reporting
Experience with data reconciliation across multiple systems
Familiarity with BI tools (Metabase, Looker, etc.)
Experience designing semantic layers or metric definitions
Prior experience as an early or founding data hire
BigQuery, dbt (or similar), Airbyte/Fivetran (or custom pipelines), Metabase, Amplitude, Stripe, Python, SQL, GCP
Competitive base salary and bonus program
Equity — meaningful ownership in what you build
High autonomy, high growth environment
Bay Area preferred (hybrid allowed)
Visa sponsorship available
We’ll consider remote
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