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.
What you get to do every single day: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.
Basic Qualifications
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.
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.
ELT (MYSQL CDC, Kafka, Snowflake, Fivetran, dbt Core & DBT Cloud, Astronomer, Alation, Montecarlo)
BI (Tableau, Looker)
Infrastructure (AWS, Kubernetes, Terraform, Github Actions)
The intelligent heart of customer experience
Zendesk software was built to bring a sense of calm to the chaotic world of customer service. Today we power billions of conversations with brands you know and love.
Zendesk believes in offering our people a fulfilling and inclusive experience. Our hybrid way of working, enables us to purposefully come together in person, at one of our many Zendesk offices around the world, to connect, collaborate and learn whilst also giving our people the flexibility to work remotely for part of the week.
As part of our commitment to fairness and transparency, we inform all applicants that artificial intelligence (AI) or automated decision systems may be used to screen or evaluate applications for this position, in accordance with Company guidelines and applicable law.
Zendesk is an equal opportunity employer, and we’re proud of our ongoing efforts to foster global diversity, equity, & inclusion in the workplace. Individuals seeking employment and employees at Zendesk are considered without regard to race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, medical condition, ancestry, disability, military or veteran status, or any other characteristic protected by applicable law. We are an AA/EEO/Veterans/Disabled employer. If you are based in the United States and would like more information about your EEO rights under the law, please click here.
Zendesk endeavors to make reasonable accommodations for applicants with disabilities and disabled veterans pursuant to applicable federal and state law. If you are an individual with a disability and require a reasonable accommodation to submit this application, complete any pre-employment testing, or otherwise participate in the employee selection process, please send an e-mail to [email protected] with your specific accommodation request.
Skills Required
- Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field
- 10+ years of experience in data engineering, data architecture, or distributed systems
- At least 5 years of experience in technical leadership roles
- 7+ years of experience building, working with, and maintaining scalable data platforms
- 5+ years of experience with cloud columnar databases such as Snowflake or Google BigQuery
- 3+ years of production experience with dbt and modern ELT pipelines
- Extensive experience with data technologies including Airflow, Snowflake, Fivetran, dbt, AWS, GitHub Actions, Docker, Kubernetes, and Terraform
- Hands-on experience with AWS services including S3, Glue, EMR, Athena, Snowpipe, Kubernetes, and Terraform
- Understanding of data governance, security controls, and access management
- Experience with observability, alerting, and incident management for data systems
- Intermediate experience with Python, Go, Java, or Scala; Python is primarily used
- Excellent communication skills for collaboration with executives, data scientists, analysts, and engineers
- Ability to translate business requirements into technical solutions with data scientists, analysts, and stakeholders
- Proven ability to mentor staff and senior engineers
- Ability to work effectively in a complex, occasionally interruption-driven environment with geographically distributed teams and customers
Zendesk Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Zendesk and has not been reviewed or approved by Zendesk.
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Fair & Transparent Compensation — The company states a commitment to publishing base pay ranges and advancing pay equity, helping employees gauge fairness. Public messaging on pay equity and transparency signals structured, consistent compensation practices.
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Leave & Time Off Breadth — Time away programs include flexible PTO, dedicated well‑being days, emergency time off, and pregnancy loss leave. Parental leave is described as generous, and travel support exists for reproductive care where access is restricted.
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Healthcare Strength — Benefits language highlights comprehensive medical, dental/vision, mental health access, and an employee assistance program. These offerings are positioned as part of holistic wellbeing support across regions.
Zendesk Insights
What We Do
Zendesk software was built to bring a sense of calm to the chaotic world of customer service. Today we power billions of conversations with brands you know and love. We advocate for digital first customer experiences— and we stick with it in our workplace. Over 5,000 employees worldwide are collaborating from kitchen tables, home offices, co-working spaces, and Zendesk workspaces to make one team.
Why Work With Us
We know one desk doesn’t fit all. At Zendesk, we prioritize remote work because we believe great work happens anywhere. Digital first is more than where we work though. We give our employees flexibility and choice in both where and how they work while also trusting them to be a team player.
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