Senior Data Engineer

Mô tả Công việc
About Us
We're a world-leading smart mobility SaaS tech company with almost 2,000,000 active users. Our teams are collaborative, vibrant, and fast-growing, and all team members are empowered with the freedom to influence our products and technology.
Are you curious, innovative, and passionate?
Do you take ownership, embrace challenges, and love problem-solving?
We're looking for a Senior Data Engineer who will help us build robust pipelines and infrastructure to process and analyze audio and video data, revolutionizing the way our customers use connected technology.
Your Role
Design and develop efficient pipelines to ingest audio and video data from various sources, such as microphones, cameras, and cloud storage.
Implement techniques for cleaning and preprocessing data, including noise reduction, normalization, and video frame extraction.
Extract meaningful features from audio and video data, including acoustic, visual, and temporal attributes.
Oversee labeling and annotation of datasets for machine learning models.
Build and maintain scalable data pipelines using tools like Apache Airflow and Apache Spark.
Design efficient storage solutions for large-scale data, including data lakes and object storage.
Collaborate with machine learning engineers to integrate models into production environments.
Continuously monitor and optimize data pipelines for performance and scalability.
Design and develop efficient pipelines to ingest audio and video data from various sources, such as microphones, cameras, and cloud storage.
Implement techniques for cleaning and preprocessing data, including noise reduction, normalization, and video frame extraction.
Extract meaningful features from audio and video data, including acoustic, visual, and temporal attributes.
Oversee labeling and annotation of datasets for machine learning models.
Build and maintain scalable data pipelines using tools like Apache Airflow and Apache Spark.
Design efficient storage solutions for large-scale data, including data lakes and object storage.
Collaborate with machine learning engineers to integrate models into production environments.
Continuously monitor and optimize data pipelines for performance and scalability.