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Data Engineering Consultant

3 months ago

Edmonton, Canada CGI Full time
Position Description:

Ready to join an outstanding culture that cares about diversity and inclusion, corporate social responsibility and gives the freedom to innovate, influence decisions, and chart an exciting career? Are you a data engineering professional interested in working within a talented and collaborative team developing Cloud, Data, and AI solutions for leading global clients? Do you often find yourself in discussions about disparate and scattered data sources and feel the need to create robust data engineering infrastructure to remedy this disorder? If you have the technical expertise to build pipelines and enable data engineering or ETL within large organizations, a genuine problem-solving aptitude, and the mindset required to collaborate with diverse clients and team members, we are looking for you. We offer the opportunity to work with Canada’s largest Information Technology Outsourcing and Management Consulting Company in a fast-paced and energetic atmosphere where your personal and professional growth is accelerated by your desire and sense of commitment. CGI’s continuous growth is a testament to the confidence clients place in the company and the dedication of our professionals. At CGI, all employees are called members because they feel a powerful sense of ownership and accountability. Members at CGI are building their own company – in + offices worldwide. CGI has the global resources, expertise, stability, and dedicated professionals needed to achieve results for clients – and for members; that is why an astounding 90 percent of them are CGI shareholders.


Benefits include a share purchase program, profit sharing, wellness credits, training and development programs, and flexible work schedules.


Your future duties and responsibilities:
  1. Strategic collaboration with clients to explore and deliver key data engineering solutions.
  2. Delivery in areas of data preparation and transformations, as well as ETL or ELT development, whether on-premises or in the cloud.
  3. Building data engineering pipelines on the cloud (Azure, AWS, GCP, etc.).
  4. Working in a professional environment with large, complex, and voluminous datasets, as well as scattered data sources.
  5. Utilizing SQL or advanced SQL skills and Python skills as needed.
  6. Gathering client requirements, coding, and implementing data solutions.
  7. Implementing proof of concepts to demonstrate value, then packaging and scaling full-fledged data engineering both on-premises and in cloud environments.
  8. Supporting and collaborating with other data engineers or data scientists on the team regarding technical knowledge.
  9. Pipeline Construction: Build and maintain scalable and high-performance data pipelines using Azure Data Factory, or other Azure services to transport data seamlessly across systems.
  10. Data Storage and Management: Manage data storage in Azure, selecting appropriate storage solutions (like Azure Data Lake, Azure SQL Database) to ensure data is stored efficiently and securely.
  11. Data Cleaning and Transformation: Develop scripts or use ETL tools for data cleaning and transformation to prepare data for analysis, ensuring data quality and accessibility.
  12. Automation and Optimization: Automate pipelines and optimize data retrieval using Python and Azure Data Factory.
  13. Monitoring and Maintenance: Monitor data pipelines and performance, troubleshoot any issues, and ensure systems are up to date with the latest security patches.

Required qualifications to be successful in this role:
  • 5 years or more of practical experience in data engineering.
  • Experience working on at least one modern cloud platform (Azure, AWS, GCP, etc.).
  • Excellent communication and teamwork skills.

Desirable qualifications:
  • Experience in data engineering with ML projects.
  • Databricks platform experience and certifications are highly desirable.
  • Experience with pipeline management tools and data workflow tools such as Airflow, Jenkins, etc.
  • Experience with a variety of tools such as: Azure Data Factory, Google Dataflow, Qlik Compose, AWS Data Pipeline or Glue, Talend, Microsoft SSIS, IBM Datastage, Informatica.
  • Microsoft PowerBI, Tableau

Skills:

Azure Data Lake, Cloud Computing, Data Analysis

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