Staff Data Platform Engineer

New

Reality DefenderCybersecurity, AI

NYC HQ or Remote (U.S.)Full-TimeStaff

SalaryBase salary range: 210,000 - 260,000, based on experience, level, and location. Role includes equity.

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Job Details

Bring 8+ years building data platforms or data infrastructure, including staff- or lead-level ownership of production pipelines handling high-volume data.
Have hands-on experience with distributed processing such as Spark or Ray and lakehouse platforms, ideally Databricks, on cloud object storage.
Have production experience with Databricks, Unity Catalog, Delta Lake, and Spark on AWS, including performance tuning, access controls, and operational troubleshooting.
Have experience with large unstructured media such as video, audio, or images, including decoding, transcoding, and metadata at scale.
Have experience building reproducible ML datasets using versioning, lineage, data quality validation, and orchestration tools such as Airflow, Dagster, or Databricks Workflows.
Bring strong Python and SQL skills and the judgment to own ambiguous platform problems end-to-end.
Have experience with infrastructure and deployment automation using Terraform and CI/CD, including AWS storage, IAM, compute, and Databricks resources.
Synthetic data generation pipelines or running generative models at scale is a nice to have.
Experience supporting ML research teams with training and evaluation data, especially for multimodal models, is a nice to have.
Experience in security, fraud, trust and safety, an adversarial domain, or a Series A or B startup is a nice to have.

Own the end-to-end data platform for AI research, from continuous ingestion and preprocessing through governance and export.
Scale the Databricks lakehouse and distributed processing stack, making architecture and build-versus-buy decisions.
Build reusable infrastructure for in-house synthetic data generation across modalities.
Implement dataset versioning, lineage, metadata, quality checks, and stable train/evaluation splits.
Own pipeline reliability, observability, and processing cost per dataset.
Partner with ML researchers to turn data needs into self-serve tools.
Mentor engineers and improve data engineering practices through code review, testing, documentation, and runbooks.
Work with Security and compliance stakeholders on data handling, licensing, and access controls.
Shape the long-term data platform roadmap with engineering and research leadership.

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