Top Data Jobs in Sydney

Design, build, and deploy scalable Generative AI and LLM production applications on GCP. Implement RAG architectures with vector databases, develop high-throughput APIs and microservices, collaborate with cross-functional teams, and apply prompt engineering, model optimization, and performance evaluation to ensure reliable AI solutions.

Lead design and delivery of enterprise-scale generative AI and multi-agent systems. Define reference architectures, RAG and vector knowledge layers, integration fabrics, observability, governance, and Responsible AI frameworks. Assess readiness, build roadmaps, coordinate cross-functional teams, and mentor engineering talent.

Lead design, build, and deploy production-grade Generative AI solutions. Architect and optimise RAG pipelines and vector DBs, integrate AI via secure APIs/microservices, manage prompt lifecycle, implement MLOps/versioning/monitoring, and mentor engineering teams.

Lead the electrical design for data centre projects, manage multidisciplinary teams, engage clients, mentor junior engineers, and incorporate industry trends.

Build and operate the data infrastructure powering reporting, analytics, and AI. Design and maintain dbt models, Dagster pipelines, and ingestion connectors. Scale AWS-based data infrastructure with Terraform, improve orchestration reliability, enable self-service analytics, enforce data quality via CI/CD and testing, and collaborate with analysts and data scientists to support production ML use cases.

Design and maintain scalable data pipelines and real-time streaming systems on AWS, enable ML/LLM-powered customer features, integrate Amazon Bedrock and models into agentic workflows, automate ML lifecycle steps, collaborate with ML, data science, and product teams to ship production-grade solutions.

Lead single-threaded product and technical ownership for Client Intelligence & Data, defining APIs and event-driven architectures to deliver merchant-facing analytics, KPIs, and reports. Drive platform strategy toward AI-driven insights, enforce production reliability and automated QA, champion You Build It You Run It culture, partner cross-functionally to scale multi-tenant analytics capabilities, and prioritize work using evidence-based frameworks.

The role involves owning product feature development, participating in system design, and maintaining software performance in an agile setting.

Lead a unified data and experimentation framework across the company, engaging with core business lines to support data-driven decision-making, while ensuring data quality and governance.

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.

Lead presales enterprise architecture for Data/AI and Applications, designing integrated multi-tower solutions (data platforms, GenAI, MLOps, application modernization, cloud, integration, AIOps). Drive account planning, C-suite workshops, bid leadership, value cases, demos, portfolios, and in-contract innovation while coaching teams and ensuring delivery and commercial alignment.

Contract AI/Data Platform Engineer to build 2-3 AI-enabled use cases for sales and commercial teams, design Azure OpenAI integrations, establish RAG patterns, standardize Power BI datasets/semantic models, expose data via APIs, embed solutions into workflows, and deliver reusable patterns for the internal team within tight timeframes.

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.

Abstract and enter clinical data from healthcare records, apply clinical operations policies, troubleshoot VPN/MFA access, maintain confidentiality, and provide guidance to less-experienced team members while ensuring data quality and workflow adherence.

Build and enhance enterprise analytics and visualization solutions: develop interactive dashboards, data-driven apps, transform and prepare data with Python, implement frontend visualization components with TypeScript, integrate with Microsoft and Google enterprise ecosystems, and use AI tools to accelerate development while collaborating with stakeholders and owning deliverables end-to-end.

Lead design and delivery of scalable AWS data platforms and lakehouse solutions; govern Databricks or Snowflake architectures; define modelling standards and CI/CD best practices; oversee transformations (dbt, PySpark, SQL), streaming solutions, data governance and cataloguing; guide tooling decisions, mentor teams, and support pre-sales and stakeholder engagement.

Lead architecture and delivery of scalable AWS-based data platforms (Databricks or Snowflake), define modelling standards, enforce CI/CD and engineering best practices, oversee ETL/transformations (dbt, PySpark, SQL), implement governance and lineage, advise on tooling and trade-offs, support streaming use cases, mentor teams, and contribute to presales activities.

Design and implement scalable cloud-native data architectures, build and optimise ETL and big-data pipelines using Databricks/Spark/Delta Lake, ensure data quality and governance, automate workflows with Python/Scala/SQL, and collaborate with data scientists and analysts to enable AI-driven insights.

The Lead Data Engineer will design and build cloud engineering solutions, lead data infrastructure projects, and mentor the engineering team to enhance data value for clients.

The role involves designing and building scalable systems for trade lifecycle management, maintaining data integrity, and collaborating on reporting pipelines, while also handling production support.

The Senior Analyst will use data and analytical techniques to solve business challenges, create dashboards, communicate insights, and partner with teams to enhance product features and operational processes.

The Senior Data Engineer will engineer and improve enterprise-scale data platforms on AWS, design and build end-to-end data pipelines, and optimize data processing solutions using Apache Spark and Python. Responsibilities include data governance, compliance controls, and collaboration with cross-functional teams to support analytics and data integration needs.

As a Technical Lead, you'll oversee data engineering and reporting projects, lead cross-functional teams, and ensure compliance with data governance in healthcare solutions.

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, 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.