2 months ago
Base Salary
$200k - $300k/yr
Responsibilities
- Define enterprise semantic architecture for metrics, dimensions, entities, relationships, grain, time semantics, and business context.
- Establish reusable semantic contracts for analytics, applications, and AI systems.
- Evaluate and recommend semantic platform architectures, including Snowflake Semantic Views, dbt Semantic Layer/MetricFlow, Cube, and other approaches.
- Design and build metadata-driven capabilities for semantic discovery, metric computation, query generation, governed access, versioning, and extensibility.
- Partner with AI teams on conversational analytics, Data Agents, and other AI interfaces.
- Establish golden datasets and evaluation practices for semantic interpretation, query accuracy, metric correctness, and analytical answer quality.
- Lead multi-team initiatives from architecture and proof of concept through production adoption, operational support, and continuous improvement.
- Influence build-versus-buy decisions, mentor engineers, improve contributor experience, and drive platform adoption and migration.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Data Science, or a related field, or equivalent practical experience.
- 10+ years of experience in data engineering, data architecture, distributed systems, software engineering, or related platform disciplines.
- At least 3+ years of hands-on experience implementing semantic layers.
- Experience designing and building large-scale data, analytics, semantic, developer, or platform systems used by multiple teams in production.
- Deep expertise with modern analytical warehouses and transformation frameworks, including Snowflake or Databricks, dbt, and cloud-based ELT pipelines.
- Strong software engineering fundamentals and production experience with SQL and at least one programming language such as Python, Java, Go, or Scala.
- Deep understanding of dimensional, entity-based, and analytical data modeling, including grain, relationships, time dimensions, and metric definitions.
- Ability to collaborate with executives, architects, engineers, analysts, data scientists, and business stakeholders.
- Proven ability to mentor senior engineers and influence technical decisions across multiple teams.
- Preferred experience with Snowflake Semantic Views, dbt Semantic Layer/MetricFlow, Cube, LookML/Looker, text-to-SQL, agent evaluation, MCP, conversational analytics, vendor evaluation, or adoption roadmaps.
Benefits
- Hybrid working model with purposeful in-person collaboration and flexibility to work remotely for part of the week.
- Position may be eligible for bonus, benefits, or related incentives.
Tech Stack
Categories
Data Engineering
About Zendesk
Zendesk builds a cloud-based customer service and contact center platform that unifies ticketing, messaging, voice, knowledge base, and AI-driven automation for businesses. It sells SaaS subscriptions and add-ons to companies across industries; founded in 2007 and headquartered in San Francisco, Zendesk is privately held under Hellman & Friedman after being taken private, and supports omnichannel CX and employee service workflows.