Top Data Engineer Jobs in Sydney

Build and maintain reliable cloud-based data pipelines, reusable data assets, curated datasets, and scalable data solutions. Modernize legacy processes, improve workflow performance, and implement data quality, lineage, governance, privacy, security, and resilience controls. Collaborate with cross-functional teams to support reporting, analytics, operational use cases, AI-enabled outcomes, and better customer experiences.

Support and lead L2/L3 investigations for 5G Cloud Core (UDM/CCxx) incidents, perform fault isolation and service restoration, analyze logs and network flows, collaborate with product/project teams to improve stability, manage provisioning/data operations and upgrades, and drive automation, documentation and knowledge sharing to prevent repeat faults.

Lead enterprise pre-sales engagements focused on data readiness for AI, including discovery workshops, architecture reviews, executive briefings, and technical advisory. Design and advise on scalable Snowflake, Databricks, and lakehouse platforms; translate complex data engineering concepts into business outcomes; support services sales opportunities; develop thought leadership; engage technology partners; and guide customers through data platform migrations, governance, and AI-ready data foundation strategies.

Leads pre-sales data engineering engagements, including discovery workshops, architecture reviews, executive briefings, and technical advisory work. Helps customers develop AI-ready data strategies and scalable lakehouse architectures using Snowflake and Databricks. Translates complex technical concepts into business outcomes, supports services opportunities, creates thought leadership content, engages technology partners, and advises on data platform migrations, governance, orchestration, and AI workload foundations.

Build, test, and execute automated data pipelines, ETL/ELT transformations, BigQuery staging models, file ingestion systems, reconciliation scripts, and migration workflows. The role supports Workday, finance, and enterprise system migrations through mock cycles, testing, dress rehearsals, and final cutover. Responsibilities include SQL optimization, data cleansing, error logging, security controls, test-environment reloads, and real-time cutover monitoring.

Build and maintain data models, ETL pipelines, data warehouses, and scalable data infrastructure. Implement data quality checks, monitoring, alerting, and incident resolution processes. Contribute to data platform architecture and explore AI technologies that improve engineering workflows and business insights. The role also involves workflow orchestration, task dependency management, and supporting cloud-based deployment and infrastructure automation.

Develop and maintain scalable batch and streaming data pipelines using native GCP services. Design data warehouses and models in BigQuery, write Python and SQL, and use Apache Beam or Spark for data processing. Preferred experience includes AWS or Azure integration, Kafka, containerization with Docker and Kubernetes, and GCP data governance, security, IAM, and compliance.

Build, maintain, debug, and optimize data pipelines across AWS and GCP. Support data ingestion, transformation, warehousing, testing, monitoring, database performance, backup and recovery. Collaborate with cross-functional engineering teams in an agile environment, troubleshoot production issues, contribute to cloud architecture and operational best practices, and provide technical handover and escalation support.

Own and improve the reliability, quality, governance, and observability of the company’s internal data platform. Build production data pipelines and integrations, implement testing and validation, document lineage, enable self-service analytics, and automate operational support. Collaborate with product engineers, delivery teams, and data specialists while using AI-assisted development tools to improve data engineering practices.

Designs and optimizes scalable data pipelines and architectures supporting analytics and operational needs. The role develops ETL, streaming, data quality, governance, and cloud-based workflows using modern big data technologies. Responsibilities include translating business requirements into technical solutions, monitoring data systems, improving reliability and performance, and providing technical guidance on data modeling, storage, and metadata management.

Own the end-to-end delivery of data pipelines, models, and tests using MSSQL, dbt Cloud, Snowflake, and Fivetran. Monitor data quality, lineage, and job health while developing reusable data modeling patterns and scalable solutions. Collaborate across teams, maintain strong documentation, and use AI-assisted development tools to improve productivity, testing, reliability, and code quality.

Build and maintain scalable ETL pipelines, data models, quality checks, monitoring, and reporting data products for finance and advertising revenue. Centralize enterprise, order management, and ad-server data to support forecasting and decision-making. Collaborate with product, data science, analytics, and business teams; contribute to cloud infrastructure and workflow automation; and mentor engineers.

Develops and supports data integration solutions using Informatica PowerCenter, IICS, and IDMC. Performs hands-on SQL and UNIX work, collaborates with subject matter experts and clients, communicates effectively, and works independently.

Designs, builds, and operates scalable, secure, high-performance data platforms and pipelines supporting analytics, reporting, machine learning, and real-time insights. Collaborates with analytics, data science, product, and business teams; mentors engineers; optimizes Databricks workloads; and drives architecture, engineering standards, governance, and cloud cost efficiency.

Develops advanced AI and machine learning solutions, including forecasting, reinforcement learning, optimization, generative AI, conversational AI, and graph AI. Leads predictive modeling initiatives, foundation model development, AI governance, and responsible AI practices. Partners with business and technical leaders to align AI strategies with organizational goals, establish performance metrics, and deliver measurable cost savings, efficiency improvements, and revenue growth.

Leads customer-embedded data science engagements, translating business needs into technical approaches and delivering production-grade solutions. Builds trusted relationships with senior stakeholders, operates effectively in ambiguous environments, applies industry context, and transitions work to FDE teams with clear technical direction. Owns customer outcomes end to end while driving urgency, accountability, and sustained impact.

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.

Senior Data Engineer responsible for modernizing and simplifying data platforms across legacy and cloud environments. The role involves designing Snowflake, dbt, and Azure Data Factory solutions; supporting SQL Server warehouses; building pipelines and data products; improving reliability, governance, security, lineage, and operational excellence; and partnering with stakeholders during platform transformation. The engineer will also help enable future AI and machine-learning workloads through quality data products, model-supporting pipelines, and monitoring capabilities.

Designs and administers secure, governed Google Cloud environments, focusing on IAM, RBAC, BigQuery permissions, policy tags, masking, service accounts, and least-privilege access. The role develops Terraform-based infrastructure, troubleshoots configuration issues, supports dbt and data-platform deployments, migrates governed data-access patterns, documents solutions, and guides engineers and stakeholders through cloud configuration and access challenges.

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, develop, deploy, and support scalable cloud-based data pipelines, warehouses, ETL processes, and data models. Optimize performance, reliability, and data quality while applying governance standards. Collaborate with analysts, data scientists, engineers, and business stakeholders on AI-driven solutions. Create technical documentation, resolve incidents, contribute to architecture and engineering standards, and mentor other engineers.

Lead the design and development of cloud-based data platforms, pipelines, ETL/ELT processes, data models, warehouses, and lakes using Azure and Databricks. Build integrations, optimize performance, implement governance, monitor pipelines, resolve issues, and create Power BI reports. Collaborate with product managers and technical teams to deliver scalable, reusable data solutions and document processes.

Lead the design and development of cloud-based data platforms using Azure and Databricks. Build and optimize ETL/ELT pipelines, data models, warehouses, lakes, and integrations; implement governance, monitoring, and troubleshooting; and create Power BI reports. Collaborate with product managers and cross-functional teams to deliver scalable, high-quality data solutions, while documenting processes and applying coding and DevOps best practices.

Design, build and optimise cloud data platforms and lakehouse solutions on Microsoft Azure and Microsoft Fabric. Develop scalable ETL/ELT pipelines, data models, and transformations using SQL, Spark and Python. Implement data governance, security, CI/CD and collaborate with stakeholders to enable analytics, reporting and AI use cases.

Monitor and optimize data pipeline reliability, performance, and SLA compliance. Design data quality tests, investigate incidents, perform root-cause analysis, and implement preventive measures. Apply SRE practices to improve alerting, resilience, and recovery while developing automation that reduces manual intervention. Collaborate with data engineers, researchers, and market data teams to deliver accurate, available, trusted data supporting trading and research.