Data Engineer

A startup company in the Synthetic Speech Detection field was established in 2022 by several experienced entrepreneurs in the voice recognition and synthetic speech detection fields. The company raised $7 million in a seed round, providing a runway of two and a half years. The company has several paying clients, including the Ministry of Defense and TSG.

The company's products have immense disruption potential in areas such as protection against impersonation, voice theft, voice-to-face matching, and similar areas.

The tech stack includes Python, Elasticsearch, Kafka, Hadoop, Hive, AWS, Docker, Kubernetes, MongoDB, microservices, and Airflow.

The company is located in Tel Aviv near a train line and operates in a hybrid work setup (2 days from home). The company employs 15 people.

Responsibility:

- Design, develop, and maintain scalable and robust pipelines for downloading and processing large volumes of videos.

- Optimize data processing workflows and architect solutions for performance improvements.

- Collaborate with data scientists and analysts to understand data needs and translate these requirements into data models and pipelines.

- Employ GCP services (e.g. Buckets, Compute Clusters, RDS, Lambdas, etc.) for storage, computation, and other data-related tasks.

- Create and maintain automated pipelines for collaborative data labeling (Mturk, Toloka, or similar)

- Implement data quality checks, monitoring, and alerts to ensure the integrity and reliability of data pipelines.

- Advise on and implement best practices for data governance, security, and compliance.

- Actively participate in agile development processes, including sprint planning, stand-ups, and retrospectives.

Qualifications:

- Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related field.

- 5 years of experience in data engineering with a focus on big data solutions.

- Hands-on experience with GCP.

- AirFlow, KubeFlow or other orchestration tools knowledge is a must.

- Solid understanding of data warehousing concepts, data modeling, and schema design.

- Knowledge of SQL and NoSQL databases.

- Proficient in scripting languages, such as Python or Scala.

- Experience with workflow management tools, such as Apache Airflow or AWS Step Functions.