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.
Help define how data supports AI systems, agents, and long-term intelligence.
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

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

Founding Data Engineer – Core Data Platform

Key skills