Staff Data Engineer

Zendesk Zendesk · Enterprise · Krakow, Poland +2

Staff Data Engineer role at Zendesk focused on building and evolving the product analytics data architecture. The role involves defining data models, semantic layers, and core datasets, setting standards for data quality and pipeline design, and translating business problems into scalable data solutions. The role also emphasizes technical leadership, mentorship, and driving improvements in the data platform. While AI tools are mentioned for development acceleration and potential incorporation into workflows, the core function is data engineering for analytics.

What you'd actually do

  1. Own and evolve the product analytics data architecture, including data models, semantic layers, and core datasets used across multiple teams
  2. Define and drive standards for data modeling, metric definitions, data quality, and pipeline design across the organization
  3. Translate ambiguous product and business problems into scalable, well-designed data solutions
  4. Design and build reliable, observable data pipelines and systems that support both real-time and batch analytics use cases
  5. Lead cross-team initiatives to standardize schemas, align definitions, and reduce fragmentation in product data

Skills

Required

  • 8+ years of experience in data engineering or analytics engineering
  • Deep expertise in SQL and data modeling for analytics
  • 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
  • Track record of influencing technical direction across teams
  • Experience mentoring engineers

Nice to have

  • AI-assisted tools

What the JD emphasized

  • systems level
  • influence how data is used across an entire organization
  • systems and standards
  • scalable, well-designed data solutions
  • reliable, observable data pipelines and systems
  • standardize schemas, align definitions, and reduce fragmentation
  • performance, scalability, and cost efficiency
  • data testing, monitoring, and SLAs
  • technical leadership
  • influence engineering direction
  • data can unlock new product capabilities or insights
  • AI-assisted tools to accelerate development
  • productionize AI-assisted outputs
  • incorporate AI solutions into data workflows, data products, and analytics capabilities
  • large-scale data systems
  • reusable, well-structured datasets
  • balance scalability, reliability, and cost efficiency
  • data quality, testing, and observability frameworks
  • system design skills
  • architectural tradeoffs
  • influencing technical direction across teams
  • raising the overall technical bar
  • work effectively with both technical and non-technical stakeholders