Data Engineer designing and building data pipelines at LexisNexis. Collaborating with partners for high - quality analytics and AI - ready data.
About the role
- Staff Data Engineer at CommBank responsible for building robust AWS Cloud data solutions for Finance Risk and Treasury. Join a leading Data Engineering team to solve complex data-centric problems.
Responsibilities
- Design and deliver an integrated Corporate Services data platform providing a single source of truth for timely and accurate data.
- Delivery Risk Treasury Finance Data Platform outcomes; and Overseeing the delivery of all data outcomes to support Finance Risk and Treasury.
Requirements
- Have built or worked on building out data warehouses or data lakes
- Are strong in designing and delivering robust solutions
- Are familiar with the full software development lifecycle with a focus on data ingestion process, data transformation pipelines, data integration and visualisation
- Understand data profiling, basics of numerical statistics and data quality calculations
- Has experience in handling large-scale data processing and analytics
- Can provide code quality control through peer programming, code review and automated pipeline release management
- Can coach junior engineers, share knowledge to uplift and improve software development practices
- Demonstrated expertise in solution design and discovery, with advanced skills in AWS and ETL processes
- Should have a good understanding of overall DevSecOps
- Proficient in languages such as Python and SQL
- ETL Development: Design, Develop and implement ETL processes using Ab Initio (not mandatory)
- In-depth knowledge of Oracle
- Experience in relevant Amazon data and AI services, such as: S3, RDS, Redshift, GLUE, Lambda, SageMaker, Bedrock, AmazonQ, Kendra, Neptune Augmented AI with human oversight etc.
- Certified in relevant AWS Associate or Professional certifications e.g. Data Engineering, AI, Machine Learning
- Knowledge of data architectures such as data vault 2.0
- Understanding how AI can be used to solve real-world problems through data pipelines and machine learning lifecycle management
- Building scalable and efficient data pipelines that support AI model training and inference
- Leveraging AI agents to automate and accelerate engineering tasks (highly desirable experience with effective use of Model Context Protocols (MCPs) and automation for DevSecOps)
Benefits
- Flexible work arrangements
- Professional development opportunities
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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