Data Engineer II

About the Role

Key Responsibilities

• Design, build, and maintain scalable and efficient data pipelines for processing large-scale audio and acoustic datasets.
• Collaborate with ML researchers and acoustic scientists to collect, annotate, transform, and curate high-quality training and evaluation datasets.
• Implement signal processing algorithms for feature extraction.
• Work on real-time and batch processing frameworks for streaming and static audio data.
• Support model training and evaluation through optimized data loaders and preprocessing steps.
• Ensure data quality, versioning, and reproducibility using best practices in data engineering.
• Deploy and maintain cloud-based infrastructure for data workflows (e.g., AWS, Google Cloud Platform, Azure).
• Develop tools for data visualization and annotation specific to acoustic events.

Requirements

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, Acoustics, or a related field.
• Strong experience with audio signal processing libraries (e.g., Librosa, PyDub, SciPy, torchaudio).
• Proficient in Python and relevant data engineering frameworks (e.g., Airflow, Apache Beam, Spark).
• Experience working with large-scale data pipelines and cloud infrastructure.
• Familiarity with machine learning workflows, especially in audio or time-series domains.
• Understanding of acoustic features and formats (e.g., WAV, FLAC, sampling rates).
• Strong knowledge of databases, data storage formats (e.g., Parquet, HDF5), and data management tools.

Nice to Have

• Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) for audio modeling.
• Knowledge of acoustic modeling, speech recognition, or sound classification.
• Experience with edge deployment and real-time audio processing.
• Familiarity with tools like Weights & Biases, MLflow, or DVC for ML operations.