Lead Analytics Engineer, AI Platform

About the role

Stream Companies is a full-service marketing agency building software for retail automotive dealerships. We operate an AI assistant built on Snowflake Cortex as part of our OrangeOS product suite.

When our AI assistant returns a wrong answer, the issue is almost always upstream—a frozen dealer feed or a mismatched metric definition. As Lead Analytics Engineer, you will take full ownership of the semantic data layer beneath the AI, ensuring complete data accuracy, pipeline reliability, and platform performance.

What you'll do

Semantic Layer & Snowflake Governance

• Administer Snowflake roles, grants, and objects supporting the AI platform (warehouse-level decisions remain with the central data team)

• Gain fluency in our warehouse and navigate it effectively across platform work

• Own the semantic views that encode business meaning: what counts as a sale, what counts as a lead, the grain each metric lives at, etc. An error there produces the same error in every downstream answer

• Audit, refactor, and rebuild existing data models during your first 60–90 days

Retrieval quality & pipeline reliability

• Own the levers that determine retrieval accuracy: chunk size, document parsing, metadata design, and embedding configuration

• Test chunking approaches against dealer paperwork rather than assuming defaults. Snowflake recommends chunks under 512 tokens as a starting point

• Own the ingestion pipelines feeding the platform: inventory, dealer & OEM feeds, CRM & DMS data, and marketing performance

• Monitor freshness and catch failures, so a broken feed reaches you as an alert before it reaches a client as a wrong number in the platform

Accuracy and compliance

• Reconcile AI outputs against core reporting models to guarantee data precision

• Audit vehicle offers, payment calculations, and incentives to mitigate advertising compliance risk

• Expand evaluation test sets, turning output failures into specific data corrections

• Track inference and warehouse cost-per-dealer to ensure feature scalability

Product engineering and the data team

• Work with platform engineers on how semantic layer output is queried, cached, and surfaced: what the API returns, how a metric renders in the interface, and what the product shows when a value is null or a feed is stale

• Review schema changes with backend engineers before they ship, and work through with front-end engineers how a number should be labeled and qualified on screen

• Act as the standing point of contact between product engineering and the data team, carrying platform requirements that need warehouse-level work to them and translating their constraints into decisions product can act on

• Route data defects surfaced through the assistant to the data team with enough detail to be actionable, rather than a ticket reporting that the AI was wrong

Partner delivery, stakeholders, and cost

• Act as our technical counterpart on partner-built work: define the acceptance criteria, review deliverables against them, and operate the system unassisted before an engagement closes

• Ensure all work is easily administered and explainable

• Convert business questions from product and account teams into data structures within OrangeOS

• Track inference and warehouse cost per dealer, which determines whether a feature is viable at scale

Qualifications

Required

• Snowflake Administration: Direct experience managing RBAC, roles, and grants (not query-only access)

• Semantic Modeling: 3+ years building metric layers on cloud data warehouses (Snowflake semantic views, dbt metrics, LookML, Cube, etc.) with deep SQL expertise

• Pipeline Ownership: Proven experience maintaining production pipelines, monitoring failures, and inheriting legacy models

• Python Skills: Fluent in Python for data manipulation, API integrations, and automation

• Applied GenAI Experience: Hands-on experience with LLM APIs, prompt engineering, tool call integration, and basic retrieval pipelines (independent projects count)

• Communication: Ability to clearly explain AI data behavior to non-technical executives

Preferred

• Snowflake Cortex specifically: Analyst, Search, or Agents

• Applied RAG experience: chunking strategy, embedding selection, retrieval evaluation

• SnowPro certification

• Output quality measurement using golden sets or human review

• Retail automotive or martech background, particularly exposure to dealership data