Data Engineer

Contentful Contentful · Enterprise · Denver, CO · IT

Data Engineer to design, build, and scale modern data solutions for analytics, reporting, and business decision-making. Will work with data, analytics, and business teams to develop high-quality data pipelines, models, and integrations. Operates with a product mindset, owning build and run responsibilities.

What you'd actually do

  1. Design, build, and maintain scalable data pipelines and transformations across multiple systems and sources.
  2. Develop high-quality data models that support analytics, reporting, and operational use cases.
  3. Partner with analytics, product, and business stakeholders to understand data needs and translate them into technical solutions.
  4. Implement strong data quality, validation, and monitoring processes to ensure reliability and trust in the data.
  5. Optimize data storage, processing, and performance within cloud data warehousing environments.

Skills

Required

  • 3–6+ years of experience in Data Engineering or a similar technical role
  • Strong SQL skills
  • experience working with cloud data warehouses (e.g., Snowflake, Redshift, BigQuery)
  • Experience building and maintaining ETL/ELT pipelines using tools such as dbt, Airflow, or similar frameworks
  • Proficiency in Python or another scripting language
  • Strong understanding of data modeling, data structures, and modern data architecture patterns
  • Experience with CI/CD workflows, version control (Git)
  • Demonstrated ability to collaborate with analysts, engineers, and business partners in a cross-functional environment
  • Excellent communication skills
  • a product-focused mindset

Nice to have

  • Experience with monitoring/observability tools (e.g., Monte Carlo, Datadog)
  • Experience with BI tools such as Tableau or Looker
  • Background in distributed systems, APIs, or event-based architectures
  • Exposure to data security, compliance, or privacy frameworks
  • Experience utilizing AI for development
  • Infrastructure-as-Code tools (Terraform a plus)

What the JD emphasized

  • modern data solutions
  • high-quality data pipelines
  • data models
  • clean, reliable, and actionable data
  • modern data platform
  • modern data architecture patterns
  • product mindset