What are the responsibilities and job description for the Data Engineer (Entry Level) (Hybrid) position at Jobright.ai?
Acubed is the Silicon Valley innovation center of Airbus, focusing on transforming the aerospace industry through artificial intelligence technology. They are seeking a Data Engineer to manage and optimize data pipelines, particularly for AI/ML applications, while collaborating with cross-functional teams to support computer vision initiatives.
Responsibilities
- Manage data releases, tracking data provenance, evaluating data quality, providing data insight reporting.
- Contribute to scalable data and labeling pipelines for computer vision projects.
- Manage and optimize data storage solutions to ensure efficient data retrieval and processing.
- Monitor and troubleshoot data pipeline issues, ensuring timely resolution and minimal disruption to project timelines.
- Collaborate with machine learning engineers and other data/infrastructure engineers to understand data requirements and ensure the availability of high-quality labeled datasets.
- Stretch: Develop and maintain tools and processes for data labeling, including annotation workflows, quality control, and validation procedure.
Qualification
Required
- Bachelor’s in Computer Science, Data Engineering, and 2 years of experience in a related field.
- Strong Python and SQL; Good software development fundamentals a must.
- Experience as a Data Engineer, working with data pipelines.
- Proficiency with data modeling.
- Proficiency with ETL processes and data pipeline operations.
- Proficiency with OLTP databases (e.g. Postgresql).
- Proficiency with OLAP databases (e.g. ClickHouse).
- Excellent problem-solving skills and attention to detail.
- Strong communication skills and the ability to work effectively in a collaborative team environment.
Preferred
- Experience with evaluation of ML models.
- Experience developing evaluation frameworks.
- Bonus points for experience with data pipelines for computer vision applications.
- Data labeling.
- Workflow orchestration tools, particularly Dagster.
- Distributed computing frameworks (Apache Spark, Dask).
- Containerization technologies (e.g. Docker).
- Job scheduling systems (e.g. Slurm).
