Note: The job is a remote job and is open to candidates in USA. Stream Companies is a full-service marketing agency building software for retail automotive dealerships, including an AI assistant within its OrangeOS product suite. The Lead Analytics Engineer will own the semantic data layer beneath the AI, ensuring data accuracy, pipeline reliability, retrieval quality, compliance, platform performance, and effective collaboration between product engineering and the data team.
Responsibilities
- 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
- 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
- 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
- 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
- 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
Skills
- Direct experience managing RBAC, roles, and grants (not query-only access)
- 3+ years building metric layers on cloud data warehouses (Snowflake semantic views, dbt metrics, LookML, Cube, etc.) with deep SQL expertise
- Proven experience maintaining production pipelines, monitoring failures, and inheriting legacy models
- Fluent in Python for data manipulation, API integrations, and automation
- Hands-on experience with LLM APIs, prompt engineering, tool call integration, and basic retrieval pipelines (independent projects count)
- Ability to clearly explain AI data behavior to non-technical executives
- 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
Company Overview