Design and refine dimensional data models (fact/dimension, star schemas) to support analytics and AI use cases.
Write, review, and optimize SQL used across the warehouse and semantic layer.
Build and evolve the semantic or metrics layer to ensure consistent, trusted business definitions.
Partner directly with data engineers to review implementations, challenge design decisions, and improve data pipelines.
Debug data issues across the stack, from ingestion through transformation to consumption.
Modify or extend APIs and services to make data and functionality accessible for downstream systems and AI workflows.
Build connectors or lightweight services that enable systems to interact programmatically.
Translate business and product requirements into practical data models and system designs.
Identify gaps in the current architecture and drive pragmatic improvements without overengineering.
Support AI and agent-driven use cases by ensuring data is structured, accessible, and reliable.

Requirements

7–10 years of experience in data engineering, analytics engineering, or data architecture in production environments
Deep, hands-on SQL skills and are comfortable working directly in complex warehouse environments
Experience working across multiple modern data warehouse platforms (e.g., BigQuery , Snowflake, Redshift, Databricks) and understand their tradeoffs
Strong experience modeling data using Kimball-style dimensional modeling (fact/dimension design, star schemas)
Built or maintained semantic layers or metrics layers that support BI and downstream applications at scale
Strong programming skills (Python preferred) and can build or modify APIs, services, or connectors when needed
Comfortable reviewing, challenging, and improving engineering implementations
Understand how data moves across systems end-to-end, from ingestion through transformation to consumption
Comfortable operating in ambiguous environments and taking ownership of the details that make systems actually usable
Exposure to AI/ML or LLM-based workflows and understand how data platforms need to evolve to support them.

Benefits

Hybrid Work schedule
Team member appreciation events and recognition programs
Volunteer opportunities
Monthly IT stipend
Casual dress code
On-demand pay options: Access your pay as you earn it, to cover unexpected or even everyday expenses
All the traditional benefits like health insurance, 401k/401k match, employee assistance programs and time away

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

SQLdimensional modelingfact/dimension designstar schemasPythonAPI developmentdata modelingdata pipeline optimizationdata architecturedata warehousing

Soft Skills

problem-solvingcollaborationcritical thinkingownershipadaptabilitycommunicationreviewing implementationschallenging design decisionsimproving engineering implementationsoperating in ambiguous environments