RESPONSIBILITIES

• Data Related Responsibilities
  
• Architect, engineer, deploy and maintain data pipelines (Airflow DAGs) that are fault tolerant, temporally consistent, idempotent, replayable, and generally awe-inspiring
  
• Engineer tested and automated data transformations using PySpark, SQL, and Pandas
  
• Ensure the highest standard of data governance by crafting data contracts and service level agreements, automating data lineage tracking, data cataloging and runtime validations

Technical Responsibilities

• Ensure high code quality and engineering standards
  
• Work with Jenkins for continuous integration and deployment, Docker for containerization, Git for version control, and Kubernetes for deployment
  
• Write infrastructure as code scripts with Terraform to support and improve our data lakes AWS infrastructure
  
• Engage with technical challenges in the domains of storage, pipelining and schema management
  
• Work on problems related to data access and security
  
• Collaborate with other teams and contribute code to other technical projects when necessary
  
• Provide rigorous code reviews and help manage our repositories
  
• Write comprehensive tests and resolve errors in a timely manner

Non-Technical Responsibilities

• Take end-to-end ownership of data pipelines, ensuring that every stakeholderâ??s business needs are well understood and delivered accordingly
  
• Support peers as necessary, both within and outside of your team
  
• Act as a subject matter expert for all Data Engineering related matters within the company
  
• Mentor peers and contribute meaningfully to the technical culture at client

REQUIREMENTS

• Relevant academic background and/or verifiable domain expertise in Data Engineering.
  
• A minimum of 2 years programming experience, preferably in high-level Object-Oriented or Functional languages. Fluency in Python is a major asset
  
• Experience working with cloud based infrastructure and DevOps, AWS based work experience an asset
  
• Extensive experience working with batch data pipelining frameworks such as Airflow or Luigi, experience with stream processing frameworks an asset
  
• Deep understanding of data lakes, data warehouses or other analytics solutions
  
• Deep understanding of data transformation techniques and ETL scripting, knowledge of Spark and Pandas a strong asset
  
• Extensive experience writing and optimizing SQL queries
  
• Domain expertise in architecting and maintaining distributed data systems
  
• Knowledge of source control with Git, CICD pipelining, testing, containerization and orchestrated deployment
  
• Experience working in an Agile ecosystem, an asset
  
• Strong written and verbal communication skills in English, French an asset

SKILLS

• Highly analytical and detail oriented
  
• Creative thinker with excellent problem solving abilities
  
• Ability to thrive in a fast-paced, performance-driven environment
  
• Team player with solid interpersonal skills