Data Engineer, Calm

• We’re looking for someone who is comfortable with ambiguity, assesses what needs to be done, and delivers with the right balance of velocity and technical debt
• Demonstrated experience with the following languages/technologies/frameworks (or equivalents):
• Python
• Docker
• Kubernetes
• GCP and/or AWS
• Relational DBs / SQL
• Data Warehouses and Lakes, such as Bigquery, Databricks, or Snowflake
• Experience in designing and building data pipelines that scale
• Strong communication skills, with the ability to convey technical solutions to both technical and non-technical stakeholders
• Experience working effectively in a fast-paced, agile environment as part of a collaborative team
• Ability to work independently and as part of a team
• Willingness and enthusiasm to learn new technologies and tackle challenging problems
• Pragmatism: balancing scrappiness and rigor
• This role typically requires 5+ years of related experience

Desirable

• As a Data Engineer, you’ll leverage all sorts of data, from application event streams to product databases to third-party data, to help stakeholders create products and answer business questions
• Work with business stakeholders to understand their goals, challenges, and decisions
• Assist with building solutions that standardize our data approach to common problems across the company
• Incorporate observability and testing best practices into projects
• Assist in the development of processes to ensure our data is trusted and well-documented
• Effectively work with data analysts on refining the data model used for reporting and analytical purposes
• Improve availability and consistency of data points crucial for analysis
• Some past projects include:
• Standing up a reporting system in BigQuery from scratch, including data replication, infrastructure setup, dbt model creation, and integration with reporting endpoints
• Creating a user-level feature store and related API endpoints to support machine learning tasks such as content recommendation and persona creation
• Remodeling a critical data pipeline to decrease our model count by 50% and reduce run time by 83%
• Integrate our Data Warehouse with 3rd party applications for personalization that reaches tens of millions of customers
• Revamping orchestration and execution to reduce critical data delivery times by 70%