Senior Data Engineer

Senior Data Engineer – GCP / dbt / SQL

Sydney / Melbourne | Contract | Hybrid

$800-1000 including super DOE

We’re looking for a Senior Data Engineer to join a growing data team and help lead the development of new capabilities across audience building, customer segmentation and modern data products.

This is an opportunity to join at an early stage of a new capability, helping shape both the technical approach and the engineering standards around it.

You’ll be hands-on technically while also providing direction, mentoring other engineers and working closely with Product and business stakeholders.

What you’ll be doing

• Lead the design and delivery of scalable data engineering solutions supporting audience building and segmentation.
• Develop robust data models and transformation pipelines using dbt and modern cloud technologies.
• Work across cloud data platforms to ingest, transform and expose data for downstream products and applications.
• Help define the architecture and technical direction for new data capabilities.
• Work closely with Product, Analytics and Engineering to translate commercial requirements into scalable technical solutions.
• Establish and improve data engineering standards, modelling practices and development patterns.
• Mentor and support mid-level engineers, helping raise engineering capability across the team.
• Lead technical initiatives from discovery through to delivery.
• Contribute to decisions around whether capabilities should be built internally or supported through third-party products/platforms.
• Help the team develop more AI-native ways of working, including evaluating how AI and agent-based approaches can be incorporated into data workflows.
• Validate technical approaches and AI-generated outputs using strong engineering judgement.
• Help establish pragmatic standards for how emerging technologies are used across the team.

What we’re looking for

• Strong commercial experience as a Senior Data Engineer.
• Excellent dbt experience.
• Strong SQL and data modelling skills.
• Experience designing and delivering modern cloud data platforms.
• Strong experience with one or more of:
• GCP / BigQuery
• Snowflake
• Databricks
• Redshift
• Strong understanding of data warehousing, modelling and data lifecycle principles.
• Experience designing scalable data pipelines and production data solutions.
• Demonstrated ability to lead technical initiatives and mentor other engineers.
• Strong communication skills and the ability to work closely with Product and business stakeholders.

Particularly valuable experience

• Audience building / customer segmentation
• CDP platforms
• Marketing or customer data
• Building data products
• Data activation
• Customer analytics
• Experience working in a product-led environment

Experience in these areas is particularly relevant for the new capability, although strong Data Engineering fundamentals remain the priority.

AI & modern engineering

The team is moving towards a more AI-native way of working, and we're interested in engineers who have started thinking about what that means in practice.

You don't need to be an AI specialist.

What's more important is having some exposure to:

• Using AI/LLMs within engineering workflows
• AI-assisted development
• Agentic workflows
• Validating AI-generated outputs
• Thinking about how AI should be safely and effectively embedded into engineering teams
• Establishing practical standards for AI-enabled development

For the right person, there will be an opportunity to help shape how the team approaches this rather than simply adopting an existing playbook.

The type of engineer we're looking for

This isn't a role for someone who wants to be handed a fixed list of requirements.

The team needs someone who can operate in an environment where the solution isn't always defined yet.

You'll be expected to:

• Understand the business problem, not just the technical requirement.
• Challenge assumptions constructively.
• Work directly with Product and commercial stakeholders.
• Make pragmatic architectural decisions.
• Balance speed, scalability, cost and maintainability.
• Bring ideas and fresh thinking to the team.
• Mentor less experienced engineers.
• Help establish structure without introducing unnecessary enterprise complexity.

Experience in a scale-up, product-led or commercially focused environment would be valuable.

Environment

The team is building out new capability and is still considering the balance between buying an existing product and building internally.

Regardless of the eventual approach, there is a significant data engineering component and the team needs engineers who can help shape the right solution.

The wider organisation is also investing in more robust data foundations while maintaining a pragmatic, commercially focused approach.