Design, build, and optimise scalable Databricks Lakehouse solutions across AWS, Azure, and GCP
Develop robust data ingestion, transformation, and orchestration pipelines using Databricks (Spark, Delta Lake, Workflows)
Build high-quality data models to support analytics, reporting, and AI/ML use cases
Implement medallion architectures (bronze, silver, gold) and modern data engineering patterns
Collaborate closely with clients to translate business requirements into well-architected, actionable data solutions
Support or implement dbt, CI/CD pipelines, Git-based workflows, and engineering best practices
Ensure strong data quality, governance, lineage, security, and performance optimisation within Databricks environments.
Work alongside analytics, governance, and AI consultants to deliver cohesive, end-to-end solutions
Contribute to reusable assets, accelerators, and internal frameworks that strengthen Intelligen’s Databricks capability
Mentor junior engineers and positively influence client delivery and engineering standards

4–6+ years’ experience in data engineering or analytics engineering
Strong hands-on experience with Databricks (Spark, Delta Lake, Workflows), ideally in production environments
Experience with at least one major cloud platform: AWS, Azure, or GCP
Strong SQL skills and experience building complex data transformations
Familiarity with modern data stacks — e.g. Databricks, Snowflake, dbt, cloud data lakes, orchestration tools
Experience working across the full data lifecycle: ingestion → transformation → modelling → consumption
Consulting, stakeholder-facing experience, or cross-functional delivery exposure
Knowledge of DevOps concepts, version control, and/or CI/CD in data environments
Excellent communication, problem-solving, and collaboration skills
Sydney-based, with ability to work on-site with clients as required
A mindset of curiosity, delivery excellence, and continuous learning

Consultant – Data Engineer

Key skills