Aurora aims to solve complex problems in data engineering and infrastructure to support the development and deployment of self-driving technology, enabling a safer, more efficient, and accessible future in mobility and logistics.
Requirements
- Proficiency in at least one programming language commonly used for data engineering (e.g., Python, Go or C++).
- Solid experience with big data processing frameworks like Apache Spark, Flink, Kinesis Data Stream, or similar technologies.
- Hands-on experience with cloud platforms (AWS, GCP, or Azure) and their data services (e.g., S3, Redshift, BigQuery, Glue).
- Strong knowledge of SQL and experience working with relational and NoSQL databases.
- Intermediate knowledge of data analytics infrastructure, including data transformation tools such as DBT and visualization frameworks and tools
- Experience with building and managing data pipelines using an orchestrator like Apache Airflow.
- Experience with data warehousing solutions like Snowflake or data lake architectures.
Responsibilities
- Design, build, and maintain robust and scalable data pipelines and ETL/ELT processes to ingest, transform, and load data from various sources into our data warehouse.
- Develop and manage data infrastructure components using AWS cloud services and infrastructure-as-code tools like Terraform.
- Collaborate with data scientists, analysts, autonomy engineering teams and product teams to understand their data needs and build solutions that meet their requirements.
- Optimize data processing systems for performance, reliability, and cost-efficiency.
- Implement monitoring, alerting, and logging for data pipelines and infrastructure to ensure operational stability.
- Champion best practices in data governance, data quality, and security.
Other
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- 3+ years of professional experience in software engineering, with a focus on data-related projects.
- Able to systematically approach open-ended questions to identify pragmatic data solutions that scale
- Able to work effectively in a highly cross-functional, fast-moving and high-stakes environment
- Proven ability to communicate technical, data-driven solutions to both technical and non-technical audiences across stakeholders
