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We have numerous positions available in Thales Alenia Space''s Digital Competence Centre in Luxembourg. Based in Belval, Thales Alenia Space in Luxembourg develops state-of-the-art digital space solutions, using big data, artificial intelligence and cybersecurity technologies to address complex end-to-end systems'' challenges.
We are currently looking for a Senior DataOps Engineer.
What we can do together
As Senior DataOps Engineer, for a cross-domain software project involving Earth Observation and digital modelling, you will be a key member of an Agile development team working with diverse datasets high performance computing, and real-world use cases.
You will act as an architect of data pipelines, facilitating the flow of data from various sources to end-users, such as data scientists and analysts. This role is crucial for ensuring that data is accessible, reliable, and of high quality, thus enabling fast and informed decision-making.
Your responsibilities will include:
Design and implementation of data pipelines: Develop processes for extracting, transforming, and loading (ETL) data, ensuring seamless data management.
Automation of data flows: Use automation tools and processes to minimize manual intervention and errors, enhancing overall efficiency.
Data quality assurance: Establish and execute data validation and cleaning procedures to maintain data integrity and quality.
Monitoring and troubleshooting: Monitor data pipeline performance, proactively troubleshooting issues to minimize disruptions in data availability.
Continuous integration: Implement continuous integration practices for smooth code merging and establish automation for quick, reliable deployments.
Data Workflow Management: Leverage container orchestration platforms (e.g., Kubernetes, Helm, Docker) and data workflow management tools (e.g., Argo Workflows, Airbyte or Airflow) to optimize environments and processes.
Data traceability and performance: Manage logging tools (e.g., OpenSearch, Elasticsearch) for effective data traceability and performance monitoring.
Resource management: Utilize cloud deployment practices (Azure), and declarative tools like Terraform for efficient resource management.
API Development: Ensure adherence to security best practices and data governance principles while developing performant APIs using languages like Python, Rust, Go, Typescript Java etc.
Automation and Integration: Develop CI/CD pipelines to automate the build, testing, and deployment of software applications in support of machine learning or high-performance computing initiatives.
Knowledge Sharing and Continuous Improvement: Continually learning and sharing best practices across teams to benefit the entire organization.
About you
We are looking for a candidate with previous experience in data engineering, cloud engineering or a closely related role. The ideal candidate will have:
Proactive and detail-oriented DataOps Engineer with a strong expertise in designing and implementing data pipelines;
Proficient in container orchestration, cloud deployment, and data workflow management tools;
Familiarity with geospatial data tools (e.g., GDAL, Rasterio, Geoserver) and exploration tools (e.g., Jupyter Notebooks, Geopandas);
Knowledge of data management tools like Apache Spark, Kafka, and Airflow;
Understanding of data modeling concepts, database technologies (SQL, NoSQL), and system infrastructure;
Knowledgeable in security best practices and CI/CD pipeline development using tools like Kubernetes and Docker;
Collaborative team player adept at addressing specific data needs alongside data scientists and analysts;
Strong communicator, eager to learn and adapt to emerging data technologies.
We are looking for skilled and ambitious people as we continue to build up our team and create a positive and collaborative work environment. If this role sounds interesting to you, don''t hesitate to reach out to us and learn more!
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