Top Data Engineer Jobs in Sydney

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 secure, scalable batch and real-time data pipelines for payment and transaction data. Integrate APIs, databases, files, event streams, and third-party platforms; develop Snowflake data objects, tests, orchestration, monitoring, reconciliation, and recovery processes. Optimize platform performance, apply governance and security practices, troubleshoot issues, and provide rotational on-call support while partnering with analysts and stakeholders in Agile teams.

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.

Join Speechify's AI team as a Software Engineer responsible for data collection and infrastructure management to enhance model training operations.

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.

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.

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 the design and technical direction of Immutable Audience’s data platform. Build real-time streaming infrastructure with Kafka, Flink, and ClickHouse; own BigQuery warehouse datasets and dbt transformations; and establish standards for data quality, testing, observability, cost, and latency. Mentor engineers, guide architecture decisions, communicate trade-offs to stakeholders, and integrate AI tooling into engineering workflows and platform capabilities.

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.

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.

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.

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.

Build and operate cloud-native pipelines, services, and integrations for high-volume grid, meter, telemetry, and utility data. Develop data models, ingestion workflows, observability, data quality checks, and near-real-time integrations between Flex, utility systems, and grid modelling engines. Improve customer onboarding processes and contribute to testing, CI/CD, code quality, and engineering culture while using AI-assisted development tools.

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.

Technical leader responsible for designing and operating scalable, fault-tolerant ETL/ELT pipelines and real-time stream processing using Kafka and RabbitMQ. Optimize ClickHouse schemas and queries for large financial datasets, deploy secure cloud data services, implement monitoring and automated recovery, and mentor data engineers while aligning data strategy with business objectives.