Founding Data Engineer – Core Data Platform

🕒 March 26

🏄 California – Remote

🏄 California – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

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Embedding VC

WebsiteLinkedInAll Job Openings

1 - 10 employees

Founded 2023

🤖 Artificial Intelligence

💸 Finance

Artificial Intelligence • Finance

Embedding VC is an early-stage venture capital firm based in Menlo Park, CA that backs engineering, product, and research-focused founders, with a particular emphasis on AI-driven startups. The firm maintains a curated portfolio of developer and data infrastructure companies and communicates via developer-forward channels (CLI-style website, newsletters, and LinkedIn). Embedding VC focuses on seed and early-stage investments in technology companies leveraging machine learning and AI.

📋 Description

• 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

🎯 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

🏖️ Benefits

• Competitive base salary and bonus program • Equity — meaningful ownership in what you build • High autonomy, high growth environment