Data Engineer

• Take full responsibility for defining a detailed technical solution and managing technical delivery of the data engineering team (onshore and offshore) based on detailed Business Requirements and aligned to the high level solution architecture
• Design, Build and maintain scalable and efficient detailed design and frameworks using Azure Databricks for ingestion, curation and transformation of data
• Responsible for data modelling, ETL automation, DevOps, data quality, data security and performance optimization leveraging best practices of Azure cloud platform
• Work collaboratively with key stakeholders across Business, Analytics, IT teams (local and offshore) and vendors, ensuring alignment of data engineering efforts with business priorities
• Mentor and support a team of data engineers and analysts, in development of best engineering practice and upskilling engineers in Azure databricks toolset

Technical Lead – Data Engineering

• Lead the design, evolution and delivery of scalable, resilient platforms supporting customer and banker experiences.
• Drive architecture and engineering decisions across multiple squads and technology domains.
• Establish reusable engineering patterns, frameworks and standards that improve delivery speed and quality.
• Shape platform strategy across cloud, APIs, customer engagement services, workflow orchestration and AI-enabled capabilities.
• Champion modern engineering practices including DevSecOps, observability, CI/CD and automated testing.
• Partner with product, architecture, risk and business stakeholders to deliver meaningful customer and business outcomes.
• Ensure security, privacy, operational resilience and responsible AI principles are embedded into platform design and delivery.
• Mentor engineers, influence technical strategy and foster a culture of engineering excellence.

Principal Software Engineer, Customer Channels, Data & AI

• Design and build scalable data pipelines, integrations, and transformation frameworks
• Develop and maintain cloud-based data platforms and modern data architectures
• Implement data models, engineering standards, and reusable patterns that improve delivery quality
• Support the development of data products and associated governance processes
• Collaborate with architects, analysts, product owners, and business stakeholders to deliver valuable outcomes
• Contribute to platform stabilisation initiatives by improving reliability, performance, and operational support practices
• Support data quality, metadata, and documentation activities
• Participate in solution design workshops and technical planning activities
• Mentor junior consultants and contribute to capability development across delivery teams
• Help identify opportunities for continuous improvement across technology, processes, and ways of working

Senior Data Engineer – Melbourne, Adelaide

• Design and build end-to-end data migration pipelines that extract, transform, and load core banking data from legacy systems with a strong focus on Ultradata source environments into Constantinople's platform
• Develop and maintain transformation logic and data mapping frameworks that accurately translate legacy banking data models, business rules, and product configurations into Constantinople's schema
• Build robust data validation, reconciliation, and integrity checking frameworks to ensure every migration lands with full accuracy and auditability
• Own the design and development of Constantinople's core migration data tooling, creating reusable frameworks that scale across multiple client onboardings
• Work with Snowflake as the primary analytical and staging environment building pipelines, models, and data products that support both migration execution and post-migration reporting
• Collaborate with Technical BAs to turn data mapping specifications and business rules into production-grade migration code
• Support migration dry runs, parallel runs, and live cutovers including rapid triage and resolution of data issues under time pressure
• Partner with platform engineers and solutions engineering to ensure migrated data integrates cleanly with downstream systems, APIs, and banking operations
• Build cutting-edge tools and frameworks that convert raw legacy banking data into clean, validated, and operationally ready assets on the Constantinople platform
• Champion clean, maintainable, and well-documented code and data architecture that the whole team can build on

Senior Data Engineer, Core Banking Migrations

• You find superannuation opaque and frustrating, if you had the chance to revolutionise the industry to be better for people, you would
• You love sharing your ideas and seeing what happens. At this scale-up, you're not just a small fish in a big pond. Your contributions have a direct influence on our impact and how we work
• You like to be kept on your toes - change is inevitable here. You like to tackle new challenges, and maybe even discover talents you never knew you had.
• You enjoy solving complex technical problems and finding elegant, practical solutions.
• You like taking ownership of your work and seeing a solution through from idea to production.
• You're naturally curious and enjoy continuously improving systems, automating repetitive tasks and exploring new technologies.
• You enjoy collaborating with people from different disciplines and sharing your knowledge to help the whole team succeed.

• Coach and develop talented backend engineers. You'll do this through code review, pairing, and design discussions that raise the technical bar for the team.
• Run the team’s core rituals, including standups and sprint planning, and do the day-to-day work of keeping the team unblocked.
• Build reliable customer data integrations. Design and maintain the systems that continuously sync customer data from tools like Gong, Intercom, Zendesk, Slack, HubSpot, and more into Dovetail. You'll build reliable, scalable integrations with robust authentication, rate limiting, error handling, and monitoring.
• Build our core data platform. Develop the shared data pipeline that transforms raw customer data into clean, enriched datasets used across Dovetail. You'll help create a scalable foundation that powers features and future AI capabilities.
• Create reusable data infrastructure. Build systems that allow customer data to be ingested once and reused across multiple teams and products, reducing duplicate processing and making our platform faster, more efficient, and easier to scale.
• Develop data enrichment services. Build pipelines that automatically transcribe conversations, redact sensitive information, link customer records, and extract structured insights so every product surface works from the same high-quality data.
• Scale distributed backend systems. Design event-driven architectures that process millions of customer records efficiently. You'll improve system reliability, optimise processing pipelines, and build the monitoring and observability tools that help the team quickly detect and resolve issues.

Tech Lead Manager – Data Platform

• Develop, own, & maintain the Regional Data Architecture Blueprint
• Maintain the blueprint as a living artefact, updated as strategy, structure & technology evolve
• Connect strategic business objectives to data needs through a Business Object Model
• Engage senior stakeholders to translate strategic priorities & decision-making requirements into a coherent model of data needs
• Develop logical data models as the implementation agnostic bridge between business requirements & physical design
• Translate business requirements into structured specifications for engineering & delivery teams
• Align Business Object & Data Domain models with capability frameworks & operating model design
• Mentor & support stakeholders to build internal data capability

Business Data & AI Architect – Lead

• Design, build and optimise enterprise-scale ETL and ELT pipelines
• Develop modern cloud-native data solutions using Azure Data Factory, Microsoft Fabric and Databricks
• Design and maintain scalable data warehouse and lakehouse environments
• Apply software engineering best practices including CI/CD, automated testing, version control and Infrastructure as Code
• Develop high-performing data models that support enterprise analytics and reporting
• Build trusted data foundations that enable Machine Learning, advanced analytics and Generative AI initiatives
• Develop vector database pipelines supporting Retrieval-Augmented Generation (RAG) solutions
• Ensure data governance, security, quality and compliance within a healthcare environment
• Collaborate with data scientists, analysts, architects and business stakeholders to deliver high-value solutions
• Monitor, troubleshoot and continuously improve platform reliability, scalability and performance
• Contribute to documentation, engineering standards and the ongoing uplift of enterprise data capability

Data Engineer – Medicare Mental Health Check In

• You will plan, develop and support the data engineering solutions that prepare, integrate and make data accessible for analytical and operational use across ASD
• You will work across multiple data sources and formats, build reusable NiFi components, and shape the data flow and integration strategies that the broader analytical capability depends on
• Plan and drive the development of data engineering solutions, balancing functional and non functional requirements
• Build scalable and reusable NiFi templates and processors for data ingestion and transformation
• Optimise performance and ensure high availability of data pipelines
• Integrate data from multiple sources and formats into centralised systems
• Perform data modelling to support efficient data storage, retrieval and analytics
• Provide expert advice on data integration and uplift the capability of others in applying good integration practice
• Monitor application of data standards, architectures and security to ensure compliance and scalability
• Develop standards and guidelines for data protection, disaster recovery and storage management
• Contribute to organisational policies and guidelines for data engineering
• Collaborate with cross functional teams to define data flow and integration strategies
• Lead the selection and development of data engineering methods, tools and techniques
• Develop and promote continuous integration, deployment and monitoring practices

Senior Data Engineer – NiFi

• Working within the Australian Signals Directorate (ASD), you will lead the design, development and delivery of data engineering solutions that improve how data is stored, integrated, transformed, analysed and moved across complex, mission critical environments.
• This is an expert level role at SFIA Level 6, with responsibility for shaping organisational policies, standards and design patterns that influence how data is engineered across the agency.
• You will work across the full data lifecycle, from analysing complex problems through to building, deploying and maintaining pipelines, analytical environments and data stores.
• The role also calls for strategic advice on current and future state models, data interoperability, and emerging technologies including AI based data integration.
• Build, maintain and deploy data pipelines, analytical environments and data stores that move and transform data across complex environments.
• Use and develop tools and techniques to test data engineering solutions and achieve business outcomes.
• Provide expert advice on requirements, current and future state models, and solutions that improve data interoperability, storage, analysis and transmission.
• Lead the application of analysis, design and modelling techniques to establish, modify and maintain enterprise data structures.
• Manage the iteration, review and maintenance of data requirements and data models.
• Lead the selection and development of data engineering methods, tools and techniques.

Expert Data Engineer

• Design and build a scalable, modern data warehouse (greenfield) to support business-wide analytics and future AI use cases
• Develop robust ETL/ELT pipelines to ingest and transform data from multiple sources into clean, usable datasets
• Own data modelling, schema design, and metric definitions across the business
• Partner with Product, Engineering, BI and Finance to deeply understand data needs and translate them into scalable solutions
• Drive data quality, reliability, and performance at scale
• Evaluate and optimise the data stack (build vs buy decisions, tooling, architecture)
• Support real-time data initiatives to enable faster, more informed operational decisions
• Mentor and guide a junior data engineer, with scope to grow the team over time

Senior Data Engineer

• Design, build and maintain data engineering solutions in SQL and Python on Enterprise Data Platforms
• Develop and sustain data pipelines that move data from source to consumption reliably and at scale
• Operationalise data so it is accessible, trusted and ready for use by business areas
• Fuse data from multiple sources using transformation, integration, normalisation and feature extraction techniques
• Create data structures that support downstream analytics, reporting and decision making
• Apply appropriate tools and techniques across the end-to-end data management lifecycle in line with agreed process standards and industry-specific regulations
• Build standardised tooling and techniques that drive consistency in ETL and data warehousing approaches across the team
• Carry out data quality checking and remediation to maintain confidence in the data
• Produce clear technical documentation and track work using collaboration tools
• Work in a diverse team environment, sharing outcomes and experience to support the development and growth of others
• Engage with technical and business stakeholders to understand data needs and deliver fit-for-purpose solutions

Senior Data Engineer, ETL

• Serve as the technical platform owner for our modern Data & AI platform.
• Lead the design, implementation and evolution of data engineering solutions that enable real-time data processing, IoT integration, and advanced analytics capabilities across the organisation.
• Drive innovation through Generative AI and Machine Learning capabilities, establishing frameworks and pipelines that enable predictive analytics, intelligent automation and data-driven decision making to transform patient care and operational efficiency.
• Enable data democratisation by building robust, user-friendly data pipelines and self-service analytics capabilities that empower stakeholders across the organisation to access and leverage data insights independently and securely.
• Design and implement cloud-native data architectures including data lakes, lakehouses, streaming platforms and interoperability layers.
• Establish data modelling standards, ETL/ELT processes and data quality frameworks that support scalable, reliable data operations.
• Build strong relationships with clinical, operational and technical stakeholders to understand data needs, gather requirements and implement process change management.
• Act as a trusted advisor on data engineering best practices and innovation opportunities.
• Ensure the Mission, Vision, Creed, and Values of SVHA (Compassion, Justice, Integrity, Excellence) are effectively integrated into all areas of responsibility, maintaining ethical data practices and patient-centered technology solutions.

Data Engineering Lead

• Designing, creating and managing the cloud databases and data pipelines that underpin our decoupled cloud architecture and API first approach.
• Take ownership and accountability of functional and non-functional design.
• Collaborate with the People Analytics team to deliver data products needed to support business goals.
• Use deep understanding of data modelling and data quality to identify, transform and validate data required for advanced analytics and machine learning.

Data Engineer jobs in Australia

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