Senior Staff Data Engineer

Zendesk•Madison, WI

9d•$200,000 - $300,000•Hybrid

About The Position

Our Enterprise Data & Analytics (EDA) team is looking for an experienced Senior Staff Engineer to lead the architecture and engineering of an Enterprise AI-ready semantic data platform. We are building an AI-ready Enterprise data foundation that enables analytics, applications, and AI agents to operate from the same trusted understanding of business data. Our platform includes trusted foundational data models across Customer, Finance, GTM, and Product; a governed semantic layer for metrics, dimensions, entities, relationships, and business context; and conversational analytics over enterprise data. As a Senior Staff Engineer on our EDA Engineering Team, this role sits at the intersection of data engineering, data architecture, software engineering, and AI. The successful candidate will partner with architects, data engineers, data scientists, Analysts Teams, and AI teams to establish reusable platform capabilities that are reliable, observable, secure, governed, and practical to adopt. This role is ideal for someone who can set technical direction across multiple teams while remaining hands-on with architecture, prototyping, and production delivery.

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, 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. (ex. Atscale, Cube.dev, DBT Metric Flow, etc.) 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.

Nice To Haves

Experience with one or more of Snowflake Semantic Views, dbt Semantic Layer / MetricFlow, Cube, LookML / Looker, or comparable semantic and metrics platforms.
Experience with text-to-SQL, agent evaluation, MCP, or conversational analytics.
Experience defining deterministic boundaries between governed metric computation and probabilistic AI reasoning.
Experience evaluating vendors or open-source platforms and translating architectural recommendations into an adoption roadmap.

Responsibilities

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.