Staff Data Engineer

Venice.ai•Sheridan, WY

About The Position

Venice is looking for a Staff Database Engineer to help organize, connect, and make trustworthy the company’s data. The role’s goal is for anyone at Venice to be able to query data, get an answer, and trust that it is correct. The engineer will take a broad view of the data ecosystem, from where data originates and how it moves to how it is modeled and how reliable sources of truth are established. This is an individual contributor role with significant technical ownership. The successful candidate will contribute to standards and architectural decisions, solve ambiguous data problems, and partner with Engineering, Product, Finance, Growth, and Operations to build a scalable data foundation. The operational environment is SQL-focused, using GCP CloudSQL and PostgreSQL, with high statement throughput, significant server-side SQL, and replicas for BI. The company aims to remain SQL-centric and plans to address eventual growth beyond a single PostgreSQL database through horizontal sharding on accounts. The team is phasing out ORM usage to maximize SQL flexibility. The role covers data architecture, integrity and trust, queryability, and access. It includes designing data models and schemas, improving performance and reliability, establishing canonical datasets and metric definitions, building data quality and lineage capabilities, and enabling secure self-service and AI-assisted querying. Success means that data is understandable and trustworthy, important metrics have agreed definitions, quality issues are detected automatically, and both technical and non-technical teams can confidently use company data.

Requirements

Significant experience designing and operating data systems at scale.
Expert-level SQL and relational database knowledge, with a 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 understand an existing data environment and thoughtfully improve it without unnecessarily rebuilding it.
Strong technical judgment and ability to make architectural decisions with incomplete information.
Ability to communicate complex data concepts clearly to technical and non-technical stakeholders.
High ownership and comfort operating with significant autonomy.

Nice To Haves

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.

Responsibilities

Contribute to the architecture and evolution of Venice’s data infrastructure.
Develop a 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.
Establish systems to ensure 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 to show where numbers originate and how they were calculated.
Design data structures for 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.
Partner across Engineering, Product, Finance, Growth, and Operations.