Zendesk's mission is to make Customer Experience better - for every brand, every customer, every day. Data and analytics are the engine of that vision: the vehicle through which we deliver actionable insights to the 125,000+ global brands that run on Zendesk, and to the internal teams who build the next generation of CX products on top of that data.
The Zendesk Analytics Prototyping (ZAP) team sits at the sharp end of that mission. We design and ship the fine-grained, contextually rich datasets that power Zendesk's customer-facing analytics application - and that the rest of the analytics organisation also depends on for support-operations insight, and product decision-making. Our data assets serve external customers and internal stakeholders in equal measure, but the customer-facing surface is where the quality bar is set.
We are looking for a Principal Data Engineer - someone who pairs principal-level architectural judgement with a willingness to stay close to the code, the dbt models, and the dataset semantics. Together with the tech leaders of ZAP, the customer-facing analytics application team, and the data platform team, you will set the technical direction for how our data assets are designed, built, governed, and consumed with the explicit objective of making Zendesk's customer-facing reporting the strongest in the industry. You will also lead ZAP's transition into an AI-first operating model in which data engineering and data science are practiced at the frontier of what AI now makes possible.
Be the bridge between ZAP and Zendesk's customer-facing analytics application team. This is the role's centre of gravity. Drive joint architectural alignment on the data assets that power customer-facing reporting; lead design reviews, mediate divergent viewpoints, translate complex architectural choices for technical and non-technical stakeholders, and shape the long-term roadmap of ZAP's data products in direct service of Zendesk's customer-facing analytics strategy.
Set the design patterns for ingestion, modelling, transformation, governance, and consumption with explicit trade-offs for scale, reliability, cost, and time-to-insight on the surfaces customer facing reporting actually depend on.
Stay hands-on. Build and review the highest-leverage pipelines and curated datasets yourself in Snowflake, dbt, Airflow, and Terraform. Principal here is not a title that exempts you from production code - it is a title that says your code sets the bar for what reaches Zendesk's customers.
Lead ZAP's transition into an AI-first operating model in which data engineering and data science are practiced at the frontier of what AI now makes possible - translating new capability into reliable, repeatable team practice.
Establish engineering best practice for ZAP and influence beyond it. Schema design, performance tuning, cost monitoring, (...) - codified as patterns, embedded in CI, reused across our pipelines and AI skills, and held to the standard required of data that ships to external customers.
Mentor staff and senior engineers across data engineering and data science. Grow the technical depth of the team in both disciplines.
10+ years in large-scale data engineering or analytics infrastructure, including at least 3 years at principal or staff level with a clear track record of architectural ownership.
Deep hands-on Snowflake expertise - architecture, performance optimisation, and advanced features (Snowpark, Snowpipe, Cortex, Native Apps, external functions, secure sharing).
Production fluency with the modern analytics stack - dbt, Airflow, Apache Spark, Iceberg, Terraform, GitHub Actions - and strong opinions on when to use each.
Extensive practical knowledge of OLAP warehousing and data modelling - Kimball, Inmon, wide-table, normalised - and a clear sense of which model fits which problem, including for cost-effective, performant analytics at scale.
Strong architectural judgement across ETL/ELT, batch and streaming, real-time pipelines, and data access patterns.
SQL fluency and at least one production-grade programming language (Python preferred; Go, Java, or Scala welcome).
Demonstrated ability to lead technical design reviews and drive alignment across multiple engineering teams - translating depth into strategy for diverse stakeholders, and partnering as an equal with other tech leaders rather than dictating from the side.
A bias for shipping, and for clarity. ZAP thrives in ambiguous problem spaces; the role demands proactive problem-solving, comfort with iteration, and the discipline to leave a clear trail behind every decision.
Hands-on architectural leadership migrating or evolving enterprise data platforms to cloud-native OLAP (Snowflake, BigQuery, Redshift).
SnowPro Core (or equivalent demonstrable depth).
Experience with embedded / customer-facing analytics delivery technologies and an understanding of how warehouse-side modelling choices shape the customer experience.
Experience integrating Snowflake with event-driven architectures (Kafka, Kinesis, Pub/Sub).
Experience building or operating an AI-augmented engineering practice - agentic IDE workflows (Cursor, Claude Code), prompt/skill engineering, eval design, and the discipline of treating AI artefacts as production code.
Familiarity with CRM analytics and Lean / 6 Sigma principles.
Contributions to internal or external data platform communities and a habit of thought leadership.
ELT: Snowflake, dbt, Airflow, Kafka
BI: Zendesk proprietary application, Looker
Infrastructure: AWS, Kubernetes, Terraform, GitHub Actions
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