Staff+ Data Engineer (ML Infrastructure)

Responsibilities

• Design and implement large-scale data pipelines for audio and metadata ingestion and transformation
• Own the lakehouse architecture including table formats, partitioning, and schema evolution
• Build and maintain batch and streaming pipelines using Spark, Flink, and orchestration tools
• Develop tooling for the full audio data lifecycle including quality filtering and augmentation
• Instrument pipelines with observability, data quality checks, and lineage tracking
• Set the technical bar through design reviews, pattern establishment, and documentation

Requirements

• 5+ years of experience in data engineering, ML infrastructure, or data platform roles
• Deep expertise in building distributed batch and streaming data systems in production
• Strong command of Spark, Flink, Ray, Airflow, or Dagster

Preferred Qualifications

• Direct experience with audio data pipelines and time-series features
• Familiarity with ASR, TTS, or speech enhancement model training workflows
• Experience with MLOps tooling such as DVC or LakeFS

About the Company

Sanas is pioneering the future of human communication with the world's first real-time speech AI platform capable of accent translation and noise cancellation.