Key skills
PythonGoSQLAIMachine LearningData EngineeringAnalyticsLookerBusiness IntelligenceSnowflakeBigQueryAirflowdbtTerraformKubernetesK8sDockerPostgresMySQLSales
About this role
Pantheon is a WebOps Platform that powers the open web, managing over 300,000 sites for various customers. They are seeking a Software Engineer II to enhance their data infrastructure and deliver analytics solutions that support business intelligence and operational excellence.
Responsibilities:
- Build scalable, reliable data systems while maintaining SLOs for business-critical pipelines
- Partner with other internal teams such as Product, Sales Ops, and Finance to deliver high-impact data solutions
- Support self-service tooling and a data-driven culture across Pantheon's teams
- Contribute to the technical strategy and operations of Pantheon's data platform
- Stay up-to-date with industry trends and technologies in data engineering, analytics, and modern data platforms
- Continuously improve our standard of engineering excellence by implementing best practices for data architecture, testing, pipeline reliability, and documentation
Requirements:
- 3-5+ years of experience building production services and features, ideally with a mix of Data Engineering expertise
- Ability to build maintainable components in Python and/or Go
- Comfortable using SQL with relational databases (e.g. MySQL, Postgres) and data warehouses (e.g. Snowflake, BigQuery)
- Experience working with containerization (e.g., Docker, OCI), Terraform, and Kubernetes (K8s)
- Familiarity with modern data stacks (e.g. Snowflake, dbt, Airflow, Looker)
- An understanding of the direct or indirect business value of their work, ensuring that data solutions align with company-wide goals and provide maximum impact for internal and external customers
- Familiarity with the concepts and challenges of processing large-scale datasets across distributed systems and a clear grasp of trade-offs when designing solutions for high throughput and low latency
- The ability to clearly articulate technical designs, project status, and potential risks to both technical peers and non-technical stakeholders (Product, Sales, Finance, etc.)
- Understanding of how to integrate automated test coverage and validation into a deployment pipeline
- Experience building data pipelines for AI and Machine Learning is a big plus!