Lead Data Engineer

Here is a non-exhaustive list of your daily missions; we trust you to take them in hand and enrich them in your own way:

• Participate in AWS Cloud projects (EC2, S3, Lambda, Redshift, EMR, Kinesis, DynamoDB, etc.) or other solutions hosted on an AWS architecture (Snowflake, Databricks, etc.).
• Develop and automate data ingestion pipelines with appropriate processing layers in relevant technologies (Python, Spark, Kafka).
• Industrialize Data Science algorithms.
• Design scalable and generic data schemas to meet reporting or other needs (SQL).
• Develop custom applications based on existing generic components to meet client needs (scenario scripting, training monitoring of predictive models and AI, reporting, etc.).
• Supervise and mentor junior consultants, i.e., peer code review, application of best practices.
• Assist our sales team in writing proposals and pre-sales meetings.
• Contribute to the development of our internal community.
• Participate in the recruitment of our future talents.

Requirements

The list of technologies you would be working on includes:

• Python, PySpark, or Scala Spark. Scikit-learn, MLlib, Tensorflow, Keras, PyTorch, LightGBM, XGBoost, Scikit-Learn, and Spark (to name a few).
• Data architectures and environments like Hadoop, ElasticSearch, Kafka, etc.
• AWS Big Data stack (Step Function, Lambda, ECR, S3, EC2, Code Build, Glue, automation and DevOps tools, EMR, Redshift, Athena).
• Implementation of DevOps environments and Infrastructure as Code.
• Familiarity with tools like Git, GitLab CI, Jenkins, Ansible, Terraform, Docker, Kubernetes, ML Flow, Airflow, or their equivalents in Cloud environments.