Staff Data Engineer

About Us

Venice si the world's leading consumer AI company built on principles of privacy, free speech, and user sovereignty. 

We’re building the Port City of AI, in which millions of individuals and AI agents gather, interact, and access sophisticated AI resources on a private and permissive foundation.  

Joining Venice means joining a team of unorthodox builders who believe in moving quickly, delivering a beautiful mass-market consumer product that doesn’t spy on people or censor their ideas and questions., If you’re energized by big ideas, entrepreneurial spirit, individual empowerment, and the opportunity to help shape a fast-growing company in the world’s hottest industry from the ground up, please reach out.

About the Role

Venice has a lot of data. The data exists! The opportunity is making it organized, connected, accessible, and unquestionably trustworthy. 

The north star for this role is simple: Anyone at Venice should be able to query our data, get an answer, and feel confident that the answer is correct. We're looking for a Staff Database Engineer to contribute to the architecture and technical foundation of how data is structured and used across Venice.

You'll take a broad view of our data ecosystem: understanding where data originates, how it moves, how it's modeled, where inconsistencies exist, and how we establish reliable sources of truth.

This is an individual contributor role with significant technical ownership. You'll contribute to standards, architectural decisions, solve ambiguous data problems, and partner across Engineering, Product, Finance, Growth, and Operations to build a data foundation that scales with Venice.

What You'll Contribute

Data Architecture

• The architecture and evolution of Venice's data infrastructure.
• Develop a deep understanding of our 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 that ensure our 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 we understand where numbers originate and how they were calculated.

Operational environment & Infrastructure Goals.

• Tens of thousand SQL statements per second
• Most of them deliberately single statement transactions
• Thousands of balance affecting statements a second
• Working towards a single PostgresSQL DB, with replicas for BI, trying to stay SQL centric as long as possible.
• Inevitable outgrow of the single PostgreSQL DB is planned to be addressed with scaling horizontally sharding on accounts.
• 5-15% growth month over month on DB statement count and storage.
• Significant server side SQL (views, functions, etc) with custom test tools 
• You will join a SQL focused team of engineers with high throughput finance background and deep ORM authorship and thus SQL generation experience. Note we are phasing out ORM usage for maximum SQL flexibility.
• GCP CloudSQL with all its operational benefits and restrictions. 
• Average active sessions (AAS) around 3-4, stable despite the ongoing growth have experienced. 

You should naturally ask: "How do we know this number is right?" And then build the infrastructure that lets us answer that question.

Queryability & Access

• Make it dramatically easier for people across Venice to answer questions with data. Including:

• Designing data structures optimized for both analytical and operational use cases.
• Improving query performance and reliability.
• Creating intuitive schemas and documentation.
• Building or supporting semantic and metrics layers.
• Enabling AI- and natural-language-based querying of company data.
• Establishing appropriate permissions and access controls.

Our goal isn't simply to make data technically accessible. It should be understandable and trustworthy enough that people can actually use it to make decisions.

What Success Looks Like

Success isn't simply having cleaner databases. It's creating a company where:

• Critical metrics have clear, agreed-upon definitions.
• There is an obvious source of truth for important data.
• We understand where data came from and how it was transformed.
• Data quality issues are detected automatically rather than discovered in meetings.
• Engineers can confidently build against our data infrastructure.
• Non-engineering teams can answer more questions without needing an engineer to investigate.
• AI tools can safely and reliably query well-structured company data.
• Two people asking the same question get the same answer.

Ultimately:

Ask a question → query the data → get the answer → trust the answer.

What We're Looking For

• Significant experience designing and operating data systems at scale.
• Expert-level SQL and relational database knowledge. 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 an existing data environment, understand how it evolved, and thoughtfully improve it without unnecessarily rebuilding everything.
• Strong technical judgment and the 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 Points

• 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.