Senior Data and Analytics Engineer
Senior Data and Analytics Engineer at SG Fleet in Sydney: leads design and delivery of scalable end-to-end data and analytics solutions, sets data architecture and engineering standards, mentors the team, and builds batch and streaming pipelines on the Microsoft Azure stack (Databricks, SQL Server, Power BI, Kafka/Flink).
- Compensation
- Not disclosed
- Location
- Sydney, Australia
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data analytics
- Posted
Skills
Must have
- Bachelor's degree in computer science, engineering, mathematics, statistics, or related field
- Up-to-date industry certifications in data and analytics tools, focused on Microsoft and Databricks
- Extensive experience in data engineering and analytics, delivering end-to-end solutions in complex environments
- Strong proficiency in SQL (T-SQL), Databricks, PySpark, and the Microsoft data stack (SQL Server, SSRS, Power BI)
- Experience designing modern data architectures, including Lambda and Kappa patterns
- Hands-on expertise building and optimising batch and real-time pipelines with Kafka, Azure Event Hubs, Confluent, or Apache Flink
- Experience with Azure data services: Azure Data Factory, Azure Databricks, Delta Lake, Azure Synapse Analytics
- Experience working in Agile delivery environments with cross-functional teams
- Strong data modelling capability (dimensional and semantic) and scalable visualisation solutions
- Experience working with large, complex datasets with strong analytical and problem-solving skills
- Excellent stakeholder engagement, communication, and presentation skills
Full description
Job Description
The Senior Data and Analytics Engineer leads the design and delivery of scalable, end-to-end data and analytics solutions that drive strategic business outcomes, enhance customer value, and enable operational excellence. Acting as a senior technical authority within Solution Delivery, the role shapes data architecture, engineering standards, and best practices while ensuring solutions are secure, governed, and future ready. Through strong stakeholder collaboration and technical leadership, the role also uplifts organisational capability and champions the effective use of data as a critical enterprise asset.
Job Requirements
- Bachelor’s degree in computer science, engineering, mathematics, statistics, or a related field
- Relevant and up to date industry certifications in data and analytics tools and technologies with a focus on Microsoft and Databricks
- Extensive experience in data engineering and analytics, delivering end-to-end solutions in complex environments
- Strong proficiency in SQL (T-SQL), Databricks, PySpark, and the Microsoft data stack including SQL Server, SSRS, and Power BI
- Proven experience designing and implementing modern data architectures, including Lambda and Kappa patterns
- Hands‑on expertise in building and optimising batch and real‑time data pipelines, including streaming solutions using technologies such as Apache Kafka, Azure Event Hubs, Confluent, and Apache Flink
- Extensive experience with Azure data and analytics services, including Azure Data Factory, Azure Databricks, Delta Lake, and Azure Synapse Analytics
- Experience working in Agile delivery environments, contributing to cross‑functional teams and iterative solution development
- Strong capability in data modelling (dimensional and semantic) and delivering scalable, high‑performing data models and visualisation solutions
- Experience working with large, complex datasets, applying strong analytical thinking and problem‑solving skills to deliver business insights
- Excellent stakeholder engagement, communication, and presentation skills, with the ability to translate technical concepts for non‑technical audiences
Job Responsibilities
- Lead the translation of business requirements into scalable data and analytics solutions that deliver measurable outcomes
- Design and oversee the development of data models, pipelines, and visualisations across a range of modern technologies
- Establish and uphold best practice in data engineering, including architecture, performance, security, and governance
- Collaborate with business and technical stakeholders to drive alignment, adoption, and value from data initiatives
- Provide technical leadership and guidance on data architecture, design patterns, and engineering standards across delivery teams