Top Data Engineer Jobs in Sydney

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 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.

Leads Azure-based data engineering engagements, including architecture, implementation, assessments, ingestion frameworks, data warehousing, analytics, and proof-of-concepts. Advises clients, presents to executives, supports presales, conducts workshops, reviews architectures, and contributes to delivery planning. Provides technical leadership through mentoring, hiring, onboarding, and engineering best-practice development.

Build and maintain batch and streaming data pipelines, Airflow workflows, data models, and warehouse datasets for a crypto trading platform. Improve data quality, freshness, performance, and cost efficiency through monitoring, validation, troubleshooting, and optimization. Collaborate with engineering and business users to define datasets, metrics, and documentation. This fully remote APAC role requires strong Python and SQL skills, hands-on Kafka, Spark, and Airflow experience, crypto or Web3 data engineering experience, and clear Mandarin communication.

Design, implement, support, and optimize enterprise network infrastructure for a large-scale data centre transformation program. Responsibilities include routing and switching, migration delivery, troubleshooting, performance improvements, testing, transition activities, and stakeholder engagement across technical and business teams.

Build and operate a Databricks-based data platform, including ingestion pipelines, CDC feeds, orchestration, CI/CD, governance, security, cost optimization, data quality, and observability. Partner with analytics and data science teams while creating self-service tooling. The role requires strong SQL and Python, cloud data-platform experience, API integration, production troubleshooting, and software engineering practices. Experience with Databricks, dbt, AWS, Spark tuning, streaming, security, and AI/LLM applications is preferred.

Design and deploy AI, Generative AI, machine learning, and data science solutions for consulting clients. Responsibilities include building LLM applications, RAG pipelines, AI agents, predictive and explanatory models, deep learning systems, optimization solutions, and big data processes. The role also involves model selection, production monitoring, AI governance, research into emerging AI capabilities, and collaboration with architects, engineers, data scientists, and clients.

Provides first-line technical support for production data processes used by researchers, traders, and business users. Monitors pipelines, investigates failures and data-quality issues, validates datasets, assesses business impact, coordinates resolution with engineering teams, and communicates updates to stakeholders. Maintains JIRA workflows, contributes to monitoring and documentation improvements, supports onboarding, and assists with live data collection and historical backfills.

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.

Leads the design, development, deployment, and continuous improvement of scalable Azure data solutions. Builds cloud-native platforms using Spark, Databricks, Python, and SQL; establishes CI/CD, testing, automation, and DevOps practices; resolves complex technical issues; collaborates with global stakeholders; and mentors engineers while contributing to platform architecture and roadmap decisions.

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 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.

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.

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.

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.

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.

Designs, builds, and optimizes cloud-native data platforms, batch and real-time pipelines, and AI-powered data products using Snowflake, Databricks, AWS, and Azure. The role includes data architecture, governance, quality, observability, security, performance and cost optimization, infrastructure as code, and direct client collaboration. The engineer also contributes to technical design, estimation, delivery planning, and modern AI capabilities such as semantic search and RAG.

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.

Lead the design and development of modern data platforms, focusing on scalable architecture and high-performance data pipelines while ensuring security and compliance.

Lead data strategy and architecture for AI-native SaaS, building secure, tenant-isolated context and retrieval layers, data platform, integrations and eventing. Define data contracts, models, and observability; make buy-vs-build decisions; mentor and grow a small Data team; partner with Product, Engineering, AI and Platform to enable reliable agentic AI features and compliant data access.

Design and deliver governed, production-grade data products and a unified semantic layer across Finance, People, Procurement, and Compliance. Build scalable pipelines, models, and governance (Unity Catalog, DataHub, Delta) using Databricks, dbt, SQL, and PySpark. Drive data quality, lineage, access control, and enable analytics and AI consumers.

Design, build, and maintain petabyte-scale ETL/ELT data pipelines and BigQuery data models on GCP. Collaborate with data scientists and engineers to ensure data quality, implement CI/CD for pipelines, monitor production systems, and recommend new cloud data technologies. Support streaming data processing and infrastructure-as-code for secure, scalable solutions.

The role involves designing and building cloud-native data platforms and pipelines, optimizing data storage, and consulting with clients on data solutions. It requires hands-on engineering skills and experience with modern data technologies.