Data Engineer

Job Description

Sanas is revolutionizing the way we communicate with the world’s first real-time algorithm, designed to modulate accents, eliminate background noises, and magnify speech clarity. Pioneered by seasoned startup founders with a proven track record of creating and steering multiple unicorn companies, our groundbreaking GDP-shifting technology sets a gold standard. Sanas is a 200-strong team, established in 2020. In this short span, we’ve successfully secured over $100 million in funding. Our innovation have been supported by the industry’s leading investors, including Insight Partners, Google Ventures, Quadrille Capital, General Catalyst, Quiet Capital, and other influential investors. Our reputation is further solidified by collaborations with numerous Fortune 100 companies. With Sanas, you’re not just adopting a product; you’re investing in the future of communication. We’re looking for a sharp, hands-on Data Engineer to help us build and scale the data infrastructure that powers cutting-edge audio and speech AI products. You’ll be responsible for designing robust pipelines, managing high-volume audio data, and enabling machine learning teams to access the right data — fast. As one of the first dedicated data engineers on the team, you'll play a foundational role in shaping how we handle data end-to-end, from ingestion to training-ready features. You'll work closely with ML engineers, research scientists, and product teams to ensure data is clean, accessible, and structured for experimentation and production. Key Responsibilities : Build scalable, fault-tolerant pipelines for ingesting, processing, and transforming large volumes of audio and metadata Design and maintain ETL workflows for training and evaluating ML models, using tools like Airflow or custom pipelines Collaborate with ML research scientists to make raw and derived audio features (e.g., spectrograms, MFCCs) efficiently available for training and inference Manage and organize datasets, including labeling workflows, versioning, annotation pipelines, and compliance with privacy policies Implement data quality, observability, and validation checks across critical data pipelines Help optimize data storage and compute strategies for large-scale training Qualifications : 2–5 years of experience as a Data Engineer, Software Engineer, or similar role with a focus on data infrastructure Proficient in Python, SQL, and working with distributed data processing tools (e.g., Spark, Dask, Beam) Experience with cloud data infrastructure (AWS/GCP), object storage (e.g.,S3), and data orchestration tools Familiarity with audio data and its unique challenges (large file sizes, time-series features, metadata handling) is a strong plus Comfortable working in a fast-paced, iterative startup environment where systems are constantly evolving Strong communication skills and a collaborative mindset — you’ll be working cross-functionally with ML, infra, and product teams Nice to Have : Experience with data for speech models like ASR, TTS, or speaker verification Knowledge of real-time data processing (e.g., Kafka, WebSockets, or low-latency APIs) Background in MLOps, feature engineering, or supporting model lifecycle workflows Experience with labeling tools, audio annotation platforms, or human-in-the-loop systems Joining us means contributing to the world’s first real-time speech understanding platform revolutionizing Contact Centers and Enterprises alike. Our technology empowers agents, transforms customer experiences, and drives measurable growth. But this is just the beginning. You'll be part of a team exploring the vast potential of an increasingly sonic future Show more Show less