Staff+ Data Engineer (ML Infrastructure

Staff Data Engineer

Our models are only as good as the data that trains them. As a Staff Data Engineer, you'll own the infrastructure that takes raw audio — millions of hours across accents, languages, noise conditions, and recording environments — and turns it into clean, reproducible, training-ready data 'll work directly with AI research scientists and ML engineers to design systems that move fast without breaking the data quality guarantees our models depend on.

Data pipeline & lakehouse architecture

• Design and implement large-scale data pipelines that ingest, transform, validate, and serve high-quality audio and metadata for AI model training, evaluation, and product telemetry.
• Own the lakehouse architecture — table format choices (Iceberg vs. Delta Lake), partitioning strategies, metadata management, and schema evolution — with a bias toward reproducibility and auditability.
• Build and maintain batch and streaming pipelines using Spark, Flink, and orchestration tooling (Airflow or Dagster), with a clear-eyed view of when each is the right tool.
• Extend and maintain feature store infrastructure to serve low-latency, versioned features for both training and real-time inference.

Audio data at scale

• Develop and maintain pipelines purpose-built for the unique challenges of audio data: large file volumes, time-series feature extraction, speaker and language metadata, and annotation versioning.
• Build tooling that supports the full audio data lifecycle — from raw ingestion and quality filtering through augmentation, segmentation, and training split generation — with reproducibility guarantees at every stage.
• Partner with ML engineers and research scientists to design data schemas, sampling strategies, and evaluation datasets that accurately reflect production conditions.
• Own data pipelines that feed human-in-the-loop annotation workflows — ensuring clean round-trips between raw data, labeling platforms, and training-ready outputs.

Platform reliability & governance

• Instrument pipelines with observability, data quality checks, lineage tracking, and alerting — so failures surface fast and root causes are traceable.
• Drive build vs. buy decisions for data quality, observability, and cataloging tooling with a clear framework grounded in Sanas's scale and roadmap.
• Own disaster recovery design for critical data assets — training datasets, evaluation benchmarks, and model checkpoints.

Technical leadership

• Set the technical bar for the data engineering team — review designs and code, establish patterns, and document decisions in a way that raises the floor for everyone.
• Work cross-functionally with AI research, infrastructure, product, and legal to align data architecture with business needs and regulatory requirements.
• Contribute to hiring — identify strong candidates, conduct technical interviews, and help define what great looks like for data engineering at Sanas.

• 5+ years of experience in data engineering, ML infrastructure, or data platform roles.
• Deep expertise building distributed batch and streaming data systems in production.
• Strong command of data processing frameworks:
  Spark, Flink, and Ray; and orchestrators:
  Airflow or Dagster.
• Hands-on experience with cloud data platforms — Snowflake, Databricks, or Click House — and object storage (S3, GCS) on AWS or GCP.
• Solid understanding of data lifecycle management: privacy, security, compliance, and reproducibility from ingestion through model training.
• Proven ability to work directly with ML researchers and engineers to translate model requirements into data infrastructure decisions.

• Direct experience with audio data pipelines — file handling at scale, time-series features, speaker metadata, or audio annotation tooling.
• Familiarity with ASR, TTS, or speech enhancement model training workflows and the data requirements specific to each.
• Experience with MLOps tooling — experiment tracking, dataset versioning (DVC, LakeFS), and training pipeline orchestration.