Azure Data Engineer

• As part of a team implement end-to-end modern data platforms in support of analytics and AI use cases
• Collaborate with enterprise architects, data architects, ETL developers & engineers, data scientists and information designers to lead identification and definition of required data structures, formats, pipelines, metadata, and workload orchestration capabilities
• Address aspects such as data privacy & security, data ingestion & processing, data storage & compute, analytical & operational consumption, data modelling, data virtualization, self-service data preparation & analytics, AI enablement, and API integrations
• Estimate effort and mentor junior colleagues
• Participate in technical meetings with client staff, and advise client with technical option analyses based on leading practices Specialized Competencies Supports Data Analytics team in the implementation of client delivery on specific engagements. Applies deep knowledge of technology to drive continuous improvement.

• Good communication skills, both written and verbal
• Interpersonal and relationship building skills
• Desire to develop self
• Client delivery focus
• Adaptable
• Focus on quality
• Problem solving ability
• Analytical

Qualifications:

• Bachelor’s Degree (or higher) in quantitative areas such as Computer Science, Information Management, Big Data & Analytics, or related field is desired;
• Certifications in Azure are an asset.

Experience:

• 3+ years’ experience in implementation of creative data solutions leveraging the latest in Big Data frameworks, supporting on-premise or Azure cloud to enable use cases in analytics and AI
• 3+ years’ experience with extraction, transformation and loading of data from a wide variety of traditional and non-traditional sources such as structured, unstructured, and semi-structured using SQL, NoSQL and data pipelines for real-time, streaming, batch and on-demand workloads
• 3+ years’ experience with data warehousing or data lakes.
• Ability to simplify complex technical concepts into easy-to-understand non-technical language in order to facilitate, communicate and interact with executives and business stakeholders, working with Agile development methods in data-oriented projects