Senior Data Science Engineer for Real-Time Flight Data Processing and Analysis

Há 7 dias


Lisboa, Lisboa, Portugal Consulteer Gmbh Tempo inteiro

**Job Overview:**

We are seeking an experienced Senior Data Science Engineer to join our team at Consulteer Gmbh in Lisbon. This role will involve working with big data technologies such as Spark, PySpark, and PostgreSQL Timeseries to design, develop, and maintain data processing pipelines.

**Estimated Salary Range:** €80,000 - €110,000 per annum, depending on experience.

Data Transformation and Processing:

The ideal candidate will have expertise in extracting, transforming, and loading (ETL) flight data using Spark and PySpark, applying relevant algorithms based on engineering requirements to ensure accuracy and consistency.

Algorithm Implementation:

They will develop, code, and test PySpark algorithms that meet specific definitions and requirements provided by aircraft engineers, addressing use cases such as anomaly detection, event recognition, and trend analysis in flight data.

Timeseries Data Management:

Responsibilities will also include structuring and optimizing large time-series datasets in PostgreSQL Timeseries, focusing on efficient storage, retrieval, and processing for real-time insights.

Platform Management:

The successful candidate will maintain data processing pipelines within Azure, ensuring scalability, performance, and data security for big data workloads.

API and Data Delivery:

They will publish processed data via API or other methods to ensure seamless integration with frontend applications, enabling real-time data access.

Monitoring and Optimization:

Continuous monitoring of data pipelines and workflows for performance and reliability is essential, with the aim of optimizing code and infrastructure to meet SLAs and operational goals.

Collaboration with Aircraft Engineers:

The Senior Data Science Engineer will work closely with aircraft engineers to translate their data analysis needs and specifications into PySpark algorithms, ensuring that data processing outputs align with technical and operational requirements.

Documentation and Best Practices:

Finally, they will document data transformation processes, maintain code quality standards, and contribute to best practices in big data engineering.



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