Staff+ Data Engineer (ML Infrastructure)

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You'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.</span></p><h2 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:15pt;font-weight:600;letter-spacing:0.5px;margin-top:18px;margin-bottom:4px;padding-left:0px;\"><b><strong style=\"font-size:15pt;white-space:pre-wrap;\">Job Description</strong></b></h2><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"white-space:pre-wrap;\">Data pipeline &amp; lakehouse architecture</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">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.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">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.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">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.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Extend and maintain feature store infrastructure to serve low-latency, versioned features for both training and real-time inference.</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"white-space:pre-wrap;\">Audio data at scale</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">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.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">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.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Partner with ML engineers and research scientists to design data schemas, sampling strategies, and evaluation datasets that accurately reflect production conditions.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Own data pipelines that feed human-in-the-loop annotation workflows — ensuring clean round-trips between raw data, labeling platforms, and training-ready outputs.</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"white-space:pre-wrap;\">Platform reliability &amp; governance</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Instrument pipelines with observability, data quality checks, lineage tracking, and alerting — so failures surface fast and root causes are traceable.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Drive build vs. buy decisions for data quality, observability, and cataloging tooling with a clear framework grounded in Sanas's scale and roadmap.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Own disaster recovery design for critical data assets — training datasets, evaluation benchmarks, and model checkpoints.</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"white-space:pre-wrap;\">Technical leadership</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">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.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Work cross-functionally with AI research, infrastructure, product, and legal to align data architecture with business needs and regulatory requirements.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Contribute to hiring — identify strong candidates, conduct technical interviews, and help define what great looks like for data engineering at Sanas.</span></li></ul><h2 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:15pt;font-weight:600;letter-spacing:0.5px;margin-top:18px;margin-bottom:4px;padding-left:0px;\"><b><strong style=\"font-size:15pt;white-space:pre-wrap;\">Qualifications</strong></b></h2><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">5+ years of experience in data engineering, ML infrastructure, or data platform roles.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Deep expertise building distributed batch and streaming data systems in production.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Strong command of data processing frameworks: Spark, Flink, and Ray; and orchestrators: Airflow or Dagster.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Hands-on experience with cloud data platforms — Snowflake, Databricks, or ClickHouse — and object storage (S3, GCS) on AWS or GCP.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Solid understanding of data lifecycle management: privacy, security, compliance, and reproducibility from ingestion through model training.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Proven ability to work directly with ML researchers and engineers to translate model requirements into data infrastructure decisions.</span></li></ul><h2 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:15pt;font-weight:600;letter-spacing:0.5px;margin-top:18px;margin-bottom:4px;padding-left:0px;\"><b><strong style=\"font-size:15pt;white-space:pre-wrap;\">Bonus</strong></b></h2><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Direct experience with audio data pipelines — file handling at scale, time-series features, speaker metadata, or audio annotation tooling.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Familiarity with ASR, TTS, or speech enhancement model training workflows and the data requirements specific to each.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Experience with MLOps tooling — experiment tracking, dataset versioning (DVC, LakeFS), and training pipeline orchestration.</span></li></ul>",
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