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Venice is a consumer AI company built on principles of privacy, free speech, and user sovereignty. They are building the Port City of AI where millions of individuals and AI agents interact on a private and permissive foundation.
The Staff Data Engineer role focuses on organizing, connecting, and making Venice's data accessible and trustworthy. The north star is simple: anyone at Venice should be able to query data, get an answer, and feel confident it is correct.
As a Staff-level individual contributor with significant technical ownership, you will contribute to the architecture and technical foundation of how data is structured and used across the company. You'll take a broad view of the data ecosystem: understanding where data originates, how it moves, how it's modeled, where inconsistencies exist, and how to establish reliable sources of truth.
Key responsibilities include:
**Data Architecture:** Design and evolve Venice's data infrastructure. Develop deep understanding of existing databases, schemas, pipelines, and data dependencies. Design scalable data models and schemas that make complex information intuitive and queryable. Determine how data from different systems should be connected, normalized, and represented. Identify architectural gaps and drive improvements across the data stack.
**Data Integrity & Trust:** Establish systems ensuring data is accurate, complete, consistent, and explainable. Build validation, reconciliation, testing, and monitoring into the data lifecycle. Identify and eliminate conflicting definitions and duplicate sources of truth. Establish canonical datasets and definitions for financial reports and critical business metrics. Create clear data lineage so the origin and calculation of numbers is understood.
**Operational Environment:** The infrastructure handles tens of thousands of SQL statements per second (mostly single-statement transactions), thousands of balance-affecting statements per second, and 5-15% month-over-month growth in DB statement count and storage. The team is working towards a single PostgreSQL DB with replicas for BI, planning horizontal sharding on accounts as growth continues. You'll join a SQL-focused team with high-throughput finance background and deep ORM authorship experience; the team is phasing out ORM usage for maximum SQL flexibility. Infrastructure runs on GCP CloudSQL with average active sessions around 3-4.
**Queryability & Access:** Make it dramatically easier for people across Venice to answer questions with data. Design data structures optimized for both analytical and operational use cases. Improve query performance and reliability. Create intuitive schemas and documentation. Build or support semantic and metrics layers. Enable AI and natural-language-based querying of company data. Establish appropriate permissions and access controls.
Success means creating a company where critical metrics have clear definitions, there is an obvious source of truth for important data, data lineage is understood, data quality issues are detected automatically, engineers can confidently build against the data infrastructure, non-engineering teams can answer questions without engineer investigation, AI tools can safely query well-structured data, and two people asking the same question get the same answer.
**Requirements:**
- Significant experience designing and operating data systems at scale
- Expert-level SQL and relational database knowledge with strong PostgreSQL focus
- Deep experience with data modeling, schema design, and database architecture
- Strong understanding of data integrity, quality, lineage, observability, and governance
- Experience establishing canonical datasets and company-wide metric definitions
- Strong understanding of database performance, indexing, optimization, and scalability
- Experience integrating data across complex product and business systems
- Ability to navigate existing data environments, understand evolution, and thoughtfully improve without unnecessary rebuilding
- Strong technical judgment and ability to make architectural decisions with incomplete information
- Ability to communicate complex data concepts clearly to both technical and non-technical stakeholders
- High ownership and comfort operating with significant autonomy
**Bonus:**
- Experience building semantic layers, metrics layers, or self-service analytics infrastructure
- Experience enabling LLMs or AI agents to reliably query structured company data
- Experience designing systems for high-volume API, inference, usage, or event data
- Experience with financial, billing, revenue, or other datasets where reconciliation and accuracy are critical
- Experience with real-time or streaming data architectures
- Experience in AI, crypto, or high-growth technical environments
