Top Data Engineer Jobs in Sydney

Support 5G Core and Cloud Core solutions for carrier-grade customer networks. Lead L2/L3 incident investigations, fault isolation, service restoration, root-cause analysis, and stability improvements across UDM, UDC, Kubernetes, and cloud-native platforms. Collaborate across product, project, service delivery, IMS, EPC, RAN, and BSS teams while providing customer updates, technical guidance, RCAs, automation, documentation, mentoring, and continuous improvement.

Design, develop, and own scalable big data and software solutions for Atlassian’s internal data platform. Collaborate with engineers and architects to ship features, uphold coding standards, and improve security, reliability, availability, performance, and operational excellence. Build and deploy services using distributed systems, cloud platforms, and big data technologies while applying data-driven problem solving.

Build and manage scalable, secure, high-throughput data pipelines and infrastructure on Google Cloud. Design BigQuery data models, develop batch and real-time ETL/ELT systems using Dataflow, Apache Beam, Composer, Airflow, and Pub/Sub, and support data quality, governance, CI/CD, monitoring, and troubleshooting. Collaborate with data scientists, analysts, and engineers while implementing Terraform-based infrastructure, secure network architectures, and enterprise cloud solutions.

Develop, test, and support production data solutions for actuarial modeling, reporting, and business processes. Build ETL pipelines across SQL Server and Azure, integrate actuarial and finance platforms, create reconciliation and validation reporting, maintain database environments, and provide operational support. The role also develops technical designs, ensures secure and scalable solutions, conducts code reviews, and promotes development standards and best practices.

Design and deliver scalable data platforms, pipelines, integrations, and analytics solutions using modern cloud technologies. Translate business needs into technical solutions, lead client-facing delivery, mentor consultants, support proposals, and build trusted stakeholder relationships. The role requires strong data engineering foundations, Databricks expertise, sound technical judgment, and the ability to lead projects or small technical teams in a consulting environment.

Design and lead scalable data platforms, pipelines, integrations, and analytics solutions using Databricks, Snowflake, Azure, and modern data engineering practices. Translate business needs into technical solutions, lead projects and small teams, mentor consultants, manage client relationships, shape proposals, and deliver practical outcomes through consulting and advisory engagements.

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 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 operate scalable data platforms and pipelines across their full lifecycle. Design cloud-based solutions, build reliable data products, optimize Snowflake and cloud costs, and implement orchestration, transformation, testing, security, governance, and observability. Provide technical direction, review designs and code, mentor engineers, and collaborate with architects, product owners, analysts, and visualization engineers.

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.

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.

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 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.

Develop and maintain scalable Python applications, APIs, microservices, and data pipelines using PySpark and Airflow. Deploy and operate solutions on Azure with Docker and Kubernetes, implementing monitoring, logging, alerting, and production support. Collaborate with product managers, data scientists, and engineering teams on global projects while contributing to coding standards, architecture, testing, documentation, and Agile delivery.

Lead the data organization powering frontier AI models. Build and manage a team of data and ML engineers, design web-scale crawling and ingestion architecture, develop pipelines for filtering and deduplication, secure proprietary Australian datasets, and establish data governance, privacy, and security standards. Collaborate with model-training teams to refine data mixtures and quality based on model performance.

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.

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.

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.