DATA ENGINEER (M/F)

The job of Data Engineer (M/F)

The Data Engineer defines, develops, implements and maintains the tools and infrastructures appropriate for data analysis by the Data Science teams. He/she ensures the creation of a solution that allows the processing of large volumes of data while guaranteeing their security. It represents the first link in the data processing chain.

Missions:

The Data Engineer sets up and makes operational the architecture and the Big Data infrastructures of a client.

He designs solutions that allow the processing of a very large volume of data.

The Data Engineer develops the flow of data and prepares them for their analysis. He works upstream of the Data Scientist.

His job is to program, automate and optimize the infrastructure algorithms which then allows the Data Analyst and Data Scientist teams to analyze the collected data. He also works with a Data team throughout the data processing stages.

Throughout the progress of the project, he/she is responsible for maintaining the technologies, languages and the proper functioning of the infrastructure. He must have a thorough knowledge of languages such as Javascript, Python, Scala

Technical skills

• Mastery of programming languages: Scala, Java, Python, Shell, VBA
• Knowledge of operating systems: UNIX, Linux, Solaris, Windows
• Knowledge of SQL database solutions: Teradata, Microsoft SQL Server, SAS Base, SAP Hana
• Knowledge of NoSQL systems: Elasticsearch, HBase, Cassandra, Redshift
• Knowledge of ETL processes and tools : Talent open studio, Pig Latin, Sqoop.
• Strong expertise on SQL and derivatives : SQL, HiveQL
• Mastery of massively parallel data computing frameworks: Hadoop, Spark, Kafka
• Knowledge of query performance improvement techniques and Business Intelligence systems (OLAP)  
• Know how to consolidate data, produce KPIs and build dashboards using tools such as Excel Power BI, Tableau Software, or QlikView.
• Be comfortable in cloud environments: GCP, Azure HDInsight,‍
• Be comfortable with continuous integration and deployment tools: Jenkins, git, GitHub, gitlab, CI/CD creation, docker, Ansible, kubernetes, etc...
• Have a basic level of knowledge about Machine Learning, Data science, andArtificial Intelligence in order to be able to work in collaboration with Data Scientists.
  

Soft skills

• Force of proposal,
• English
• Rigor,
• Reactivity,
• Analytical and synthesis skills,
• Writing skills,
• Excellent interpersonal skills,
• Organizational skills.

Developments

The Data Engineer can evolve as a Lead Data Engineer, Senior Data Scientist, Machine Learning Engineer.
His numerous technical skills and his openness to Big Data allow him to work in various positions.

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