Data Engineer, Sensor
3 weeks ago
Key Responsibilities:
- Work on large-scale structured and unstructured data sets to solve a wide array of challenging problems, using analytical, statistical, machine learning or deep learning approaches.
- Design and develop robust, scalable and efficient data pipelines for collecting, processing and storing large volumes of structured and unstructured data.
- Collaborate with data scientists and machine learning engineers to understand data requirements, feature engineering needs and model deployment strategies.
- Implement data pre-processing, transformation and enrichment techniques to ensure high-quality and reliable input data for machine learning models.
- Build and maintain data infrastructure, including data warehouses, data lakes and other relevant storage systems, to facilitate easy access to data for analysis and model training.
- Optimise data pipelines for performance, scalability, and cost-effectiveness, considering factors such as data volume, processing speed, and resource utilisation.
- Work closely with DevOps and IT teams to ensure proper deployment, monitoring and maintenance of data pipelines in production environments.
- Collaborate with cross-functional teams to ensure compliance with data governance, security and privacy regulations.
- Stay up-to-date with the latest developments in data engineering and machine learning technologies and contribute to the adoption of best practices within the team.
- Bachelor's or higher degree in Computer Science, Engineering or a related field.
- Proven experience as a Data Engineer, Software Engineer or related role, with a focus on building data pipelines and infrastructure for machine learning projects.
- Strong proficiency in programming languages, such as Python, Java or Scala; as well as SQL for data manipulation and analysis.
- Hands-on experience with data processing tools or frameworks, such as Pandas, SKlearn, Apache Spark, Hadoop or similar technologies.
- Familiarity with cloud platforms, such as AWS, GCP or Azure; and experience with cloud-based data services (e.g., S3, BigQuery, Data Lakes).
- Solid understanding of database systems, data modeling and ETL principles.
- Experience with containerisation and orchestration tools (e.g., Docker, Kubernetes) is a plus.
- Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment.
- Excellent communication skills and the ability to effectively communicate complex technical concepts to non-technical stakeholders.
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