Data Engineer AI and Data
Employment Info
JOB DETAILS
Requirements
- Knowledge with Data pipeline development, including familiarity of various technologies such as Databricks, Azure Data Factory, AWS Kinesis, Spark, Airflow.
- Proficiency and demonstrated experience in transformation of large complex data sets from source systems to target platforms (experience in Azure and AWS based solutions using one or more data engineering or data platform technologies required).
- Evidence of experience in modern development practices including use of code repositories, code review processes, and test frameworks
Responsibilities
- Be able to clearly explain strengths and weaknesses of data engineering options to both technical and senior business stakeholders.
- Design, build and operate scalable on-premises or cloud data engineering architecture solutions that facilitate data storage, integration, management, validation and security, supporting the entire data asset lifecycle.
- As required, coordinate the work of junior colleagues or team members to ensure structure and focus on achieving team goals.
- Develop data flow diagrams for data engineering pipelines and consumption design (e.g. with Databricks, Spark, Azure ADF, SSIS, AWS Glue, Kinesis or Lambda, and reporting in PowerBI or Tableau) for data engineering pipelines
- Translate business requirements into technical specifications for data engineering projects
- Serve as a vital link between business stakeholders and technical teams, ensuring that the development process aligns seamlessly with the intended design and objectives.
Desired Qualifications
- Ability to introduce innovative ways of organising, managing and integrating data to ensure its quality and ability to be used as a distinctive asset to generate a return.
- Leads by example and models behaviours that support a culture of safety, health and wellness.
- Excellent communication skills, and the ability to communicate and present information with purpose, clarity and impact
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