Staff Data Platform Engineer

Required

• 8+ years building data platforms or data infrastructure, including staff- or lead-level ownership of production pipelines that generate, ingest, or deliver data at high volume.
• Deep hands-on experience with distributed processing (Spark, Ray, or similar) and lakehouse platforms, ideally Databricks, on top of cloud object storage (S3, GCS, or Azure Blob).
• Production experience with Databricks, Unity Catalog, Delta Lake, and Spark on AWS, including performance tuning, access controls, and operational troubleshooting.
• Experience working with large unstructured media such as video, audio, or images, including the realities of large files, decoding and transcoding, and metadata at scale.
• A track record of building reproducible ML datasets: versioning, lineage, data quality validation, and orchestration (Airflow, Dagster, Databricks Workflows, or similar).
• Strong Python and SQL, and the judgment to own ambiguous platform problems end-to-end on a lean team.
• Own infrastructure and deployment automation using Terraform and CI/CD, including AWS storage, IAM, compute, Databricks resources, and distributed processing environments.
• Experience building synthetic data generation pipelines or running generative models (diffusion, voice cloning/TTS, video generation) at scale. (nice to have)
• Background supporting ML research teams with training and evaluation data, especially for multimodal models. (nice to have)
• Experience in security, fraud, trust & safety, or another adversarial domain, or at a Series A or B startup where scope regularly outpaces headcount. (nice to have)
• Within 12 months, you'll...
• Own a mature, self-serve data platform that researchers use to create, version, and export datasets without waiting on platform support.