Design and evolve Laurel's global large scale data platform architecture
Design and operate large-scale data pipelines processing millions of events per day.
Define best practices for data ingestion, transformation, processing, and storage.
Mentor and help grow team and our knowledge of big data best practices along with encouraging modern techniques
Work on Python API services (Django) to improve the serving of our AI infrastructure.
Build, improve, and maintain Airflow DAGs, and optimize MongoDB and PostgreSQL usage for data workloads.
Design and operate streaming data infrastructure, including migrations to modern event-driven architectures (e.g., Kafka).
Work on internal OLAP and DataLake systems (e.g. Clickhouse)
Develop and manage Kubernetes, Docker, and compute infrastructure for all of engineering.
Improve continuous integration, continuous deployment, and other automation.
Write backend code to help teams deliver functionality when there are deadlines or resource constraints.
Attend quarterly offsites (required travel), team standups, and other company meetings.
Raise the bar for engineering quality and advocate for best practices across the organization.

Strong Python skills with experience building and deploying APIs to support AI/ML teams
Experience with Airflow, Kubernetes, and AWS
Experience with machine learning infrastructure and large language models
Development experience with Terraform
Experience working in startup environments
Experience participating in a regular engineering on-call rotation
Experience with PostgreSQL and MongoDB
Experience with OLAP systems (Snowflake, Clickhouse, etc)
Experience with AI Specific Databases (Vector Databases for example)
Experience with event streaming platforms (e.g., Kafka, SQS)

Staff Data Engineer

Key skills