Job Description
Role Purpose
The Senior Data Engineer is responsible for the design, development, testing, maintenance, and documentation of data pipelines and data platforms in accordance with client requirements, system specifications, and defined technical architecture.
The role undertakes analysis of system requirements and evaluates existing data systems, identifying limitations and deficiencies in processes and methods.
The position is accountable for the full lifecycle of data solutions, including requirements gathering, design, implementation, testing, deployment, and ongoing production support.
Key Responsibilities
Data Architecture & DesignAnalyse and evaluate client data system requirementsDesign data architectures, including Lakehouse and medallion patternsDefine data models, schema structures, partitioning strategies, and storage layoutsDevelop solutions primarily using Databricks within AWS, Azure, and GCP environments
Data Pipeline DevelopmentDesign, develop, and maintain data pipeline code using Python, Scala, and SQLImplement data ingestion, transformation, and delivery processes in accordance with systemspecificationsTest,debug, and resolve defects in line with established standards and protocolsUtilise AI development tooling such as Genie Code, Copilot or Claude
Data Quality & GovernanceDevelop and implement data quality frameworks, validation processes, and monitoring mechanismsEnsure data platforms operate in accordance with defined specifications and governancerequirementsPrepareand maintain technical documentation and operational procedures
Infrastructure & Platform EngineeringDevelop and manage infrastructure using Infrastructure as Code tools (e.g.



Terraform, CDK,CloudFormation)Maintainversion-controlled infrastructure definitionsSupport CI/CD processes for automated deployment of data systemsDesign and implement integrations with enterprise systems, including identity, security, and compliance frameworks
System Integration andImprovementIdentifyand assess limitations in existing systems and platformsDesign and implement solutions to address integration, performance, and scalability requirements
Technical Strategy & AdvisoryProvide technical input into data system design, platform selection, and architecturedecisionsContributeto the development of proposals, including cost estimation and financial evaluation of technology options
Client Enablement and Knowledge TransferWork collaboratively with client teams to support implementation and operation of data platformsDevelop and maintain documentation to support ongoing system use andmaintenanceFacilitateknowledge transfer to enable client self-sufficiency
Multi-Cloud Capability
The role operates across multi-cloud environments and requires the design of data systems that are portable and scalable.
Platforms include:
AWS: Databricks, S3, Glue, Lambda, EventBridge, Redshift, IAM, VPC, KMS
Azure: Databricks, ADLS Gen2, Synapse, Data Factory, Event Hubs, Key Vault, Entra ID,



ExpressRoute
GCP: Databricks, GCS, BigQuery, Dataflow, Pub/Sub, IAM
Required Qualifications and Experience
Bachelor's degree in Computer Science, Information Technology, Software Engineering, or a related discipline
OR at least 5 years' relevant professional experience with appropriate vendor certifications
Databricks Data Engineer Associate and/or Data Engineer Professional certification
Experience with Databricks in a production workplace, including Delta Lake and Unity Catalog
Proficiency in SQL and Python or Scala
Experience in designing and implementing data quality and validation frameworks
Demonstrated ability to communicate technical concepts clearly to non-technical stakeholders
Strong software engineering practices, including testing, version control (Git), and CI/CD
Experience with Infrastructure as Code tools (e.g. Terraform, CloudFormation)
Understanding of distributed systems and data pipeline design
Ability to produce clear and comprehensive technical documentation
Demonstrated experience delivering and maintaining production data systems
Bonus Points
Experience with real-time or streaming data systems (e.g. Kafka)
Knowledge of data governance, privacy, and compliance frameworks (e.g. APRA CPG234, ISO *****, CIS Benchmarks)
Machine learning infrastructure and pipelines (on Databricks or cloud platform)
Background in platform engineering or Site Reliability Engineering
Cloud platform certifications (AWS, Azure, or GCP)
Experience contributing to technical knowledge sharing or capability development within teams

📌 Senior Data Engineer (Melbourne)
🏢 DX1
📍 Melbourne