Staff Data Engineer

Zendesk

Krakow or Lisbon

Zendesk is looking for a Staff Data Engineer to shape the data foundation that powers product decision-making at scale. This is a high-impact, high-ownership role focused on defining how product data is modeled, trusted, and leveraged across the company. You won’t just build pipelines - you will set direction. You’ll partner with Product, Analytics, and Engineering leaders to design systems and standards that enable consistent, reliable insights and drive measurable improvements in product adoption and customer retention. This role is ideal for someone who thrives in ambiguity, operates at a systems level, and wants to influence how data is used across an entire organization.

What you’ll be doing

Own and evolve the product analytics data architecture, including data models, semantic layers, and core datasets used across multiple teams
Define and drive standards for data modeling, metric definitions, data quality, and pipeline design across the organization
Translate ambiguous product and business problems into scalable, well-designed data solutions
Design and build reliable, observable data pipelines and systems that support both real-time and batch analytics use cases
Lead cross-team initiatives to standardize schemas, align definitions, and reduce fragmentation in product data
Drive improvements in performance, scalability, and cost efficiency across the data platform
Establish best practices for data testing, monitoring, and SLAs, ensuring high trust in data systems
Provide technical leadership through mentorship, design reviews, and influencing engineering direction beyond your immediate team
Partner with product and engineering leadership to identify where data can unlock new product capabilities or insights

We expect AI to be a core part of how we build and operate data systems:

Use AI-assisted tools to accelerate development (e.g., SQL generation, pipeline development, debugging, and documentation)
Apply strong data engineering judgment to validate, optimize, and productionize AI-assisted outputs
Identify opportunities to incorporate AI solutions into data workflows, data products, and analytics capabilities

What you bring to the role

8+ years of experience in data engineering or analytics engineering, with demonstrated ownership of large-scale data systems
Deep expertise in SQL and data modeling for analytics, including designing reusable, well-structured datasets
Strong experience with modern data stack tools such as dbt, Airflow, and Snowflake (or equivalent)
Proven ability to design systems that balance scalability, reliability, and cost efficiency in cloud environments
Experience implementing data quality, testing, and observability frameworks
Strong system design skills and the ability to make thoughtful architectural tradeoffs
Track record of influencing technical direction across teams, not just delivering within a single team
Experience mentoring engineers and raising the overall technical bar
Excellent communication skills and the ability to work effectively with both technical and non-technical stakeholders