Data engineer H/F

Today, Data processing has become an essential aspect inside the companies IT infrastructures. We have to be able to collect any relevant information on a given situation in a reasonable amount of time.
Improving the process, the control and the prediction of your Data are key factors for success, regardless the business sector you are in.
Askills is an IT service company aiming to realize this. We are convinced that the talents connection, the passion and the innovation enables us to aim for excellence together.
Our services are covering the following 5 areas :

• Bid Data architecture
• Data engineering
• Data Viz
• Data science
• Agile Development

Responsibilities:

• Work closely with data scientists, business analysts and product owners to gather requirements
• Design, implement, and operate any build required to clean, transform data as per Data scientists design
• Understand business use cases and implement services around the topic of BI and Data analytics
• Participate in functional analysis to support Data platform features
• Create reusable pipeline for data integration, data cleaning, data transformation...
• Deal with large volume of structured and unstructured data, JSON, XML and corresponding analysis tools
• Work with Azure technology stack including, Azure Databricks, Azure ML, Azure Container service, ADLS Gen2, Azure Synapse, Azure Functions, PowerApps
• Work on integration aspects such as authorization, authentification, connecting to traditional SQL and new age data sources

You have a Bachelor's degree in Engineering, Information Systems, Science, Technology, Data science, Math or Physics from an accredited university

Experience required

• At least 4 years of experience in implementation of data and analytics projects in Azure
• Proven experience with Azure Databricks
• Hands on experience in Python, R, Powershell, SQL
• Hands on experience in Spark (Python or Scala)
• Fluent in English

Additionnal recommended experiences

• Notions with running machine learning pipelines in production environments (MLOps)
• Familiarity with machine learning and deep learning concepts
• Familiarity with Azure ML
• Familiar experience in deploying container-based workloads and microservice architectures
• Practical experience with cloud-based platforms and their ML/DL offerings
• Understanding of infrastructure