Lead Consultant – Data Intelligence

Location: Melbourne, Sydney

Department: Data Intelligence

Location Type: HYBRID

Employment Type: FULL_TIME

• Architect and oversee the implementation of scalable cloud data platforms on AWS using services including S3, Glue, Redshift, Lake Formation, Kinesis, Lambda, and Step Functions.
• Lead solution design and technical governance across Databricks (Delta Lake, Unity Catalog, MLflow) and/or Snowflake (Snowpark, Snowpipe, data sharing, dynamic tables).
• Define data modelling standards and patterns — star/snowflake schemas, medallion/lakehouse architecture, data vault — appropriate to client context.
• Set and enforce engineering best practices: CI/CD for data pipelines, code review standards, testing frameworks, and documentation.
• Oversee data transformation workstreams using dbt, PySpark, or SQL, ensuring quality and maintainability.
• Guide platform and tooling selection decisions, producing clear business cases and technical trade-off analyses for clients.
• Drive data governance, lineage, and cataloguing implementation using tools such as AWS Glue Data Catalog, Unity Catalog, or Collibra.
• Provide architectural oversight on streaming and real-time data use cases (Kafka, Kinesis, Spark Streaming) where applicable.
• Team Leadership & Mentoring
• Contribute to pre-sales activities

• 7–10+ years in data engineering, data architecture, or analytics roles, with at least 3 years in a consulting or professional services environment.
• Proven track record of leading complex data platform deliveries end-to-end, with full accountability for outcomes.
• Deep hands-on AWS expertise: S3, Glue, Redshift, Athena, Lake Formation, Lambda, Kinesis, IAM, CloudWatch.
• Advanced proficiency in Databricks (Delta Lake, Unity Catalog, Spark optimisation)
• Strong Python and/or Scala development skills for data engineering workloads at scale.
• Expert-level SQL with experience in query optimisation across distributed systems.
• Experience with dbt for transformation pipelines in production environments.
• Demonstrated experience engaging senior stakeholders (Director/VP/C-suite) and translating business strategy into data solutions.
• Strong commercial awareness — able to manage budgets, write proposals, and contribute to practice growth.
• Excellent written and verbal communication skills; confident presenting to both technical and executive audiences.
• Experience mentoring or leading small technical teams in a delivery context.

• AWS Certified Data Engineer – Associate or Professional, or AWS Solutions Architect – Professional.
• Databricks Certified Professional Data Engineer or Solutions Architect.
• DAG Development: competency writing and maintaining Directed Acyclic Graphs (DAGs) using native Apache Airflow operators
• Exposure to data mesh, data products, or federated governance operating models.
• Familiarity with enterprise data governance platforms (Collibra, Alation, Atlan).
• Experience with ML platform components (SageMaker, MLflow, Feature Store).
• Knowledge of BI/visualisation tools: Tableau, Power BI, or AWS QuickSight.