Top Data Engineer Jobs in Sydney

Two-year structured graduate program to learn and build cloud-based data solutions using Microsoft Fabric. Support BI requests, develop data pipelines, maintain Power BI dashboards, apply Python/SQL, partner with stakeholders, and contribute to analytics and emerging AI initiatives with mentoring and certifications.

Provide configuration and technical data management services: version control, baselining, governance of engineering artefacts. Ensure lifecycle and audit compliance, use CM tools and processes, and share knowledge with customers and stakeholders.

Design, build, and operate scalable production data pipelines, curated data models, and data services. Ensure data quality, lineage, CI/CD, monitoring, performance, and regulatory auditability while providing technical leadership for platform data architecture.

Build, maintain and scale production data pipelines and platforms on GCP (BigQuery, Dataflow, Cloud Storage). Improve data quality, observability, governance and self-service capabilities; partner with engineers, analysts and product teams to design data models and datasets that inform business decisions.

Intelligen is seeking Data Engineers with 3-5+ years of experience in ETL and DWH technologies, particularly with Snowflake and dbt, to improve client data models and pipelines.

Lead design and delivery of scalable data and AI platforms across Azure and Databricks. Architect production-grade ML/LLM solutions (RAG, agentic patterns), build batch and streaming pipelines, formalize medallion/lakehouse standards, implement MLOps/DataOps (CI/CD, monitoring, orchestration), embed Responsible AI and governance, and provide technical leadership, mentoring, and cross-functional delivery.

Lead design, build and optimise enterprise data platform and pipelines (Microsoft Fabric migration, AX ERP modelling, Denodo). Ensure platform performance, governance, incident management, and mentor junior/offshore engineers while driving stakeholder delivery in an agile environment.

The Data Engineer will design and manage data processing systems, transforming raw data into actionable insights by building scalable pipelines on Google Cloud Platform technologies.

Hands-on data engineer to re-engineer and optimise dbt models and Snowflake data warehouses. Responsibilities include schema design, performance tuning, domain-aligned modelling, CI/CD, testing, documentation, and collaborating with client teams and consulting peers to deliver maintainable pipelines.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Design, build and operate scalable Lakehouse data platform and batch/real-time pipelines. Develop data assets, streaming solutions (Kafka/Flink), ETL/ELT workflows (Airflow, DBT), and cloud-native architectures on AWS, Snowflake and Databricks. Optimize SQL and Spark workloads, manage schema and performance for Iceberg tables, enforce data quality and governance, and partner with data science, analytics and product teams to deliver cost-efficient, reliable data solutions.

Lead design and delivery of scalable AI and data platforms including LLM and agentic systems, RAG, and Databricks-based lakehouse architectures. Architect batch and streaming pipelines, implement MLOps/DataOps, establish standards and guardrails, embed Responsible AI and governance, and mentor engineers while driving cross-functional delivery and platform reliability.

Design, develop, and maintain ELT/ETL Snowflake data warehouse solutions; implement data standards, CI/CD, monitoring, and production support; collaborate with product, business, and vendors; mentor junior developers and improve data engineering practices.

Design and deliver governed, scalable data products and a unified semantic layer across Finance, People, Procurement, and Compliance using Databricks, dbt, SQL, and PySpark. Implement end-to-end governance (Unity Catalog, column-level security, lineage, PII handling), drive DataHub metadata adoption, and set engineering standards for CI/CD, testing, observability, and data quality. Translate senior-stakeholder requirements into production-grade solutions used for reporting, analytics, and AI.

Lead design and build of data migration and transformation pipelines for migrating legacy billing to a modern SaaS billing platform. Implement file processing, mapping frameworks, pricing calculations, validation, reconciliation, reference data management, and automated testing to ensure accurate migration of large financial billing datasets.

Design and maintain StratFin data models, automate reporting and data workflows, build reliable pipelines with testing and monitoring, partner with Finance and Strategy to deliver scalable data products, and establish data quality standards to enable analytics and AI.

Lead design and scaling of real-time data infrastructure. Build fault-tolerant ETL/ELT pipelines, implement Kafka/RabbitMQ streaming, optimize ClickHouse schemas and queries, deploy secure cloud data services, mentor engineers, and implement monitoring and automated recovery for financial analytics.

Develop, deploy, and maintain LLM pipelines and RAG QA/search systems; design and optimize prompts and multi-agent LLM architectures; operate multi‑GPU/cluster inference; build evaluation pipelines for model quality, bias, and hallucination; collaborate with product and CS teams to integrate conversational AI.

Senior developer responsible for driving development of MYOB's SaaS platform, building web services and integrations, deploying cloud solutions (Azure/AWS), implementing TDD, DevOps, event-driven architecture and CI/CD, and collaborating with the Data & AI team to deliver AI-powered features and scalable, resilient systems.

This role involves managing data collection for training AI models, integrating infrastructure, and collaborating with scientists to improve data quality and pipeline efficiency.

The role involves designing, building, and maintaining scalable data pipelines for analytics, ensuring data quality and reliability, and mentoring other engineers. Requires collaboration with various teams to translate domain requirements into data systems.

Lead design and delivery of scalable data architecture and pipelines. Build, maintain and optimise ETL/data infrastructure, collaborate with stakeholders to translate requirements, ensure data quality and performance, and adopt modern data engineering practices and tools to enable trusted, business-ready datasets.

Design, build and maintain scalable cloud data pipelines and warehouses. Improve performance, ensure data quality and governance, support AI/ML initiatives, document systems, and collaborate with cross-functional teams while mentoring engineers.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

Build and own Overflow's data infrastructure: design ETL/ELT and CDC pipelines, data warehouses, OLAP schemas, and a unified data layer. Enable self-service analytics and dashboards, implement Databricks Unity Catalog for governance, ensure data quality and monitoring, and partner with Head of AI and cross-functional teams to prepare semi-structured data for MLOps and advanced AI workflows.