Data Engineer

The job responsibilities outlined in this document are not exhaustive and may evolve over time and be reviewed according to business needs.

ROLE DESCRIPTION SUMMARY

The Data Services team within SES has the mission to build, disseminate and support a strong data-driven decision-making culture in the company and to leverage data trends, products, and services to create superior products, resolve complex data challenges and create business value. Data Science and Machine Learning is one of the key trends actively being pursued.

The responsibilities include educating business peers, framing business problems and challenges, developing data and machine learning models to the point they are deployable, and contributing to build our data landscape and capability including the administration and configuration of the data ingestion tool i.e. Azure Data Factory as well as actively contributing to SES Data Governance initiatives and data management / analyse streams as part of this initiative.

PRIMARY RESPONSIBILITIES / KEY RESULT AREAS

• Implement the strategy for Cloud Data and BI Platforms (Azure Data Factory, Databricks and ML)
• Partnering with the Cloud Center of Excellence team on continuous development of cloud data infrastructure and related models
• Manage Data & BI users across the company for data access and modeling
• Integrating requirements with the Business Process/Product Owners to define/evaluate process enhancements and translate user requirements into technical specifications to develop/deploy
• Build a data landscape and capability including the administration and configuration of the data storage
• Empower employees and managers to develop reporting at all levels of the organization including executive management
• Obtaining relevant datasets and contributing to the development of SES data lake
• Preparing the data (cleansing, munging, wrangling)
• Exploring and visualizing (so as to make sense out) data
• Experimenting, prototyping and developing machine learning models (training and validating)
• Making machine learning models deployable
• Monitoring performance of deployed models and continuously improving them
• Building machine learning capability at SES (best practices, knowledge sharing) and keeping abreast of developments in the field
• Contributing to the formulation of SES Artificial Intelligence and Machine Learning strategy

COMPETENCIES

• Very good knowledge of cloud data platforms and tools such as Azure Data Factory, Data Lake and Databricks
• Proven experience as a Machine Learning Engineer or similar role
• Understanding of data structures, data modeling and software architecture
• Deep knowledge of mathematics, probability, statistics and algorithms
• Ability to write robust code in Python, Java and R
• Familiarity with machine learning frameworks
• Outstanding analytical and problem-solving skills
• Familiarity with an agile environment
• Good communication and presentation skills
• Excellent interpersonal skills. Ability to effectively respond to, interact and challenge all levels of the organization

QUALIFICATIONS & EXPERIENCE

• Internationally recognized university degree in Computer Science or Engineering
• Minimum 10 years’ experience working with Azure Data Factory
• Experience with cloud-based Data Platform infrastructure and resources
• Fluency in written and oral English. Other languages are considered as valuable assets
• Experience of working in an international business environment.
• An Azure certification is an asset especially within Data Engineering and/or Science.
• Knowledge about AI/ML and experience with applied AI solutions is an asset.

The job responsibilities outlined in this document are not exhaustive and may evolve over time and be reviewed according to business needs.

ROLE DESCRIPTION SUMMARY

The Data Services team within SES has the mission to build, disseminate and support a strong data-driven decision-making culture in the company and to leverage data trends, products, and services to create superior products, resolve complex data challenges and create business value. Data Science and Machine Learning is one of the key trends actively being pursued.

The responsibilities include educating business peers, framing business problems and challenges, developing data and machine learning models to the point they are deployable, and contributing to build our data landscape and capability including the administration and configuration of the data ingestion tool i.e. Azure Data Factory as well as actively contributing to SES Data Governance initiatives and data management / analyse streams as part of this initiative.

PRIMARY RESPONSIBILITIES / KEY RESULT AREAS

• Implement the strategy for Cloud Data and BI Platforms (Azure Data Factory, Databricks and ML)
• Partnering with the Cloud Center of Excellence team on continuous development of cloud data infrastructure and related models
• Manage Data & BI users across the company for data access and modeling
• Integrating requirements with the Business Process/Product Owners to define/evaluate process enhancements and translate user requirements into technical specifications to develop/deploy
• Build a data landscape and capability including the administration and configuration of the data storage
• Empower employees and managers to develop reporting at all levels of the organization including executive management
• Obtaining relevant datasets and contributing to the development of SES data lake
• Preparing the data (cleansing, munging, wrangling)
• Exploring and visualizing (so as to make sense out) data
• Experimenting, prototyping and developing machine learning models (training and validating)
• Making machine learning models deployable
• Monitoring performance of deployed models and continuously improving them
• Building machine learning capability at SES (best practices, knowledge sharing) and keeping abreast of developments in the field
• Contributing to the formulation of SES Artificial Intelligence and Machine Learning strategy

COMPETENCIES

• Very good knowledge of cloud data platforms and tools such as Azure Data Factory, Data Lake and Databricks
• Proven experience as a Machine Learning Engineer or similar role
• Understanding of data structures, data modeling and software architecture
• Deep knowledge of mathematics, probability, statistics and algorithms
• Ability to write robust code in Python, Java and R
• Familiarity with machine learning frameworks
• Outstanding analytical and problem-solving skills
• Familiarity with an agile environment
• Good communication and presentation skills
• Excellent interpersonal skills. Ability to effectively respond to, interact and challenge all levels of the organization

QUALIFICATIONS & EXPERIENCE

• Internationally recognized university degree in Computer Science or Engineering
• Minimum 10 years’ experience working with Azure Data Factory
• Experience with cloud-based Data Platform infrastructure and resources
• Fluency in written and oral English. Other languages are considered as valuable assets
• Experience of working in an international business environment.
• An Azure certification is an asset especially within Data Engineering and/or Science.
• Knowledge about AI/ML and experience with applied AI solutions is an asset.