Data Engineer - Data & AI Practice

Responsibilities

• Design, build and maintain scalable data pipelines for ingestion, transformation and monitoring across modern cloud platforms
• Develop batch and streaming data solutions to support analytics, BI reporting, data products and integrations
• Assess and improve data quality, integrity and usability across multiple internal and external sources
• Collaborate with engineers, analysts and client stakeholders to enhance data models and reporting outcomes
• Deliver robust ETL/ELT workflows with a focus on performance, reliability and maintainability
• Write and maintain tests, documentation and data standards to support production-grade delivery and handover
• Troubleshoot data issues, performing root cause analysis to ensure stable and reliable outcomes
• Contribute to CI/CD pipelines, release processes and engineering best practices
• Apply AI-assisted engineering tools to accelerate development, testing, and analysis-while ensuring quality and governance standards are met
• Actively participate in agile delivery, including estimation, peer reviews and knowledge sharing

Requirements

• 2-4 years' experience in data engineering, software engineering or cloud data delivery
• Hands-on experience with modern cloud platforms (AWS or Azure)
• Exposure to Databricks and/or Snowflake, with experience contributing to production-grade solutions
• Strong SQL skills, with working proficiency in Python and frameworks such as PySpark or pandas
• Experience building ETL/ELT pipelines and working with orchestration tools (e.g. Airflow, dbt, or cloud-native schedulers)
• Understanding of data modelling concepts and how data supports analytics and reporting
• Experience integrating data from APIs, SaaS platforms and operational systems
• Familiarity with Git-based workflows and CI/CD practices
• Strong problem-solving skills with the ability to troubleshoot across pipelines and data quality issues
• Clear communication skills, with the ability to work effectively in a consulting and client-facing environment
• Awareness of AI-assisted engineering practices and how they enhance delivery without compromising quality
• Desirable Certifications
• Databricks Certified Data Engineer Associate
• Snowflake SnowPro Core
• Cloud certifications across AWS or Azure
• Must be Melbourne-based , with the ability to work on-site with clients as required (typically a couple of days per week)
• Must hold an active NV1 Security Clearance or be eligible to obtain one
• Must be an Australian Citizen (due to clearance requirements)
• Life at Versent
• We also offer our team:
• Flexibility : We give you the freedom to excel, providing as much autonomy and flexibility around when you work so you can manage your time in a way that best suits you to get the job done.
• Access to Benefits That Fit Your Life : From salary packaging and purchased leave to novated leasing options, we offer a range of benefits designed to suit different lifestyles and priorities. There's something for everyone.
• Health & Wellbeing Programmes : We focus on you as a whole person, with wellbeing initiatives that are to support you mentally, physically, emotionally and financially.
• Connection : Regular social activities across your team and the organisation.
• Recognition : We offer several reward & recognition programmes to acknowledge the amazing work that you do!
• Career opportunity : We provide the opportunity to work on

Benefits

Additional Information

As a Data Engineer in our Data & AI Practice, you'll play a hands-on role in delivering scalable, high-quality data solutions that power analytics, BI, and emerging AI use cases. Working in a fast-paced consulting environment, you'll partner with internal teams and customer stakeholders to design, build and optimise modern data platforms. You'll contribute directly to delivery-owning pipeline development, improving data quality, and enabling trusted, production-ready data assets. You'll also leverage AI-augmented and agentic workflows to accelerate delivery, improve analysis, and enhance engineering outcomes-while maintaining strong technical judgement and accountability for production performance.

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