Enterprise Semantic Platform Architect

Zendesk, Inc.; Austin (TX); Híbrido; USD 200.000 - 300.000; Jornada completa

Descripción de la vacante

Zendesk, Inc. is seeking a Senior Staff Data Engineer to lead architecture and engineering of an AI-ready semantic data platform.

You will shape enterprise semantic models, contracts, and governance across Customer, Finance, GTM, and Product domains, partnering with multiple teams to deliver scalable data foundations. You will guide platform direction, mentor engineers, and ensure production delivery with measurable data quality and governance.

Formación

• Bachelor's or Master's degree in Computer Science, Data Engineering, Data Science, or related field, or equivalent practical experience.
• 10+ years of experience in data engineering, data architecture, distributed systems, software engineering, or related platform disciplines, with significant technical leadership experience.
• At least 3+ years of hands on experience in Semantic Layer implementation.
• Demonstrated experience designing and building large‑scale data, analytics, semantic, developer, or platform systems used by multiple teams in production environments.
• 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, Scala, or similar.
• Deep understanding of dimensional, entity‑based, and analytical data modeling, including grain, relationships, time dimensions, and metric definitions.
• Excellent communication skills and the 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.

Responsabilidades

• Define the enterprise semantic architecture for metrics, dimensions, entities, relationships, grain, time semantics, and business context over trusted foundational data models.
• Establish reusable semantic contracts so analytics, applications, and AI systems operate from consistent business definitions and governed data products.
• Evaluate and recommend the long‑term semantic platform architecture, including Snowflake Semantic Views, dbt Semantic Layer / MetricFlow, Cube, and other approaches.
• Design and build metadata‑driven platform capabilities for semantic discovery, deterministic metric computation, query generation, governed access, versioning, and extensibility.
• Define how conversational analytics systems consume enterprise semantics and structured data, partnering with AI teams on building Data Agents and other AI interfaces.
• Establish golden datasets and evaluation practices for semantic interpretation, generated‑query accuracy, metric correctness, and analytical answer quality.
• Lead complex, multi‑team initiatives from architecture and proof of concept through production adoption, operational support, and continuous improvement.
• Influence build‑versus‑buy decisions, mentor engineers, and raise the technical bar across Data Engineering, Analytics, BI, and AI.
• Treat the platform as an internal product by improving contributor experience, documentation, onboarding, adoption, and migration from duplicate implementations.

Conocimientos

Semantic Layer implementation

SQL

Python/Java/Go/Scala

Cloud data warehouses

Mentoring engineers

Educación

Bachelor's or Master's degree in Computer Science, Data Engineering, Data Science, or related field

Herramientas

Snowflake

dbt

LookML / Looker

MetricFlow

Descripción del empleo

Zendesk, Inc. is seeking a Senior Staff Data Engineer to lead architecture and engineering of an AI-ready semantic data platform.

You will shape enterprise semantic models, contracts, and governance across Customer, Finance, GTM, and Product domains, partnering with multiple teams to deliver scalable data foundations. You will guide platform direction, mentor engineers, and ensure production delivery with measurable data quality and governance.