5+ years of experience in data engineering, software engineering, or DevOps
Proficiency in workflow orchestrators such as Airflow, Dagster, or Prefect
Experience with major data platforms including Snowflake, Databricks, BigQuery, or in-house HDFS-based solutions
Skilled at building data infrastructure using GCP, AWS, or comparable cloud providers
Comfortable managing and deploying services on Kubernetes
Practical experience with Terraform or Pulumi for automating infrastructure
Advanced programming abilities in Python and Go are integral to this role, along with strong SQL skills (Java experience also considered)
Proven experience building, owning, and iterating on data products or data pipelines based on consumer feedback, not just building infrastructure in isolation
Experience designing systems for reuse across multiple teams
Good communication skills and a team-oriented, collaborative approach, comfortable working directly with the people who consume what you build
Experience building a data platform or data product suite from the ground up
Experience integrating or deploying ML models into production systems (e.g., classification, suggestion, or extraction models)
Experience with RAG (Retrieval-Augmented Generation) systems and vector databases
Familiarity with ML infrastructure tooling such as MLflow
Experience building or deploying agents
Experience with data modeling and data product approaches such as Data Mesh, Kimball, Inmon, Data Vault, or similar
Experience working with streaming data systems
Familiarity with Prometheus or other time-series databases used for monitoring
Experience with business intelligence (BI) tools such as Omni or Power BI
Python, Go, Snowflake, DBT, PostgreSQL, Kubernetes, Terraform, Prometheus, Google Cloud Services, Omni, Hex
The ideal candidate is a strong software and data engineer with good taste & judgement, someone who knows what good looks like, and subscribes to a “strong opinions, weakly held” mindset