Top Data Jobs in Sydney

Interns will work alongside analysts, collecting and cleaning sports data, analyzing trends, and gaining insights into the sports consultancy industry.

Sell Salesforce Agentforce and Data 360 solutions to enterprise customers by sourcing and qualifying opportunities, conducting technical discovery, crafting POVs, collaborating with cross-functional teams, and demonstrating how AI-driven customer engagement and Data 360 deliver scalable business impact. Meet and exceed technical sales targets while translating complex AI capabilities into business outcomes.

Serve as the primary technical interface for data centre cooling, leading chilled water and liquid cooling solution design, technical pricing, and bid management. Influence early-stage design, ensure performance and resilience, develop cost models and value-engineered proposals, and align cross-functional stakeholders to win complex thermal infrastructure projects.

Build and own Overflow's data infrastructure: design ETL/ELT and CDC pipelines, data warehouses, OLAP schemas, and a unified data layer. Enable self-service analytics and dashboards, implement Databricks Unity Catalog for governance, ensure data quality and monitoring, and partner with Head of AI and cross-functional teams to prepare semi-structured data for MLOps and advanced AI workflows.

Take end-to-end ownership of backend features and SaaS services in a microservices architecture. Design, implement, deploy, and operate scalable, reliable Node.js services. Work with Redis Streams, MongoDB, Kubernetes, and ensure test coverage, performance tuning, and collaboration with cross-functional teams.

Lead data-driven strategy and growth initiatives by solving ambiguous problems, partnering with cross-functional leaders, building revenue and impact models, enabling data-driven decisions, and communicating actionable insights to influence company-wide goals.

Lead the architecture, governance and adoption of Unilever's workforce digital twin and AI knowledge layer. Define canonical workforce and finance data products, manage integrations, data quality and reconciliation, build retrieval and drift monitoring, and manage vendors to ensure trusted data for People Products and AI-enabled decisioning.

Lead end-to-end solution architecture for data, analytics and AI initiatives; translate enterprise strategy into scalable designs; embed security, privacy, governance and responsible AI; provide hands-on leadership across data platforms, integration, modelling, serving and reporting; guide teams on design patterns, risks and architectural roadmaps in a regulated environment.

Lead local data management and the technology stack to deliver and optimise 40+ lead-gen programs. Oversee a 350K+ contact database, ensure data quality, governance, reporting, workflows and CRM alignment, support account/delivery teams, and manage remote data operations resources.

Support the Data Products team to define and deliver data-driven products and an intelligence layer. Collaborate with engineering, data and commercial teams to shape roadmap, drive discovery, delivery, adoption, and continuous improvement of scalable data solutions that create customer and business value.

Lead integration of client portfolio data into the Portfolio Accounting System (PAS), ensuring data governance, transformation logic, validations, reconciliations and issue triage. Collaborate with Product, Operations, SMEs, QA and customers to define requirements, perform UAT, troubleshoot data mapping issues, and maintain compliance and secure data handling.

Lead enterprise Finance data, analytics, and AI strategy to improve speed, quality, consistency, and usefulness of financial data and insights. Define future-state architecture, set standards and governance, act as a Finance CoE, partner with Technology and business unit Finance heads, provide thought leadership on high-impact initiatives, and uplift capability across Finance without centralising day-to-day analytics delivery.

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 development and deployment of scalable machine learning models and analytics products. Partner with stakeholders to define problems, engineer features, run experiments, and communicate insights. Mentor cross-functional teams, integrate models into production, and contribute to organisational data science standards, tools, and best practices.

Lead analytics to improve player experience, engagement, retention, and monetization. Analyze player lifecycle, run cohort and funnel analyses, design and evaluate A/B tests, build dashboards, define product metrics, investigate KPI changes, and partner with product, marketing, engineering, and data teams to deliver data-driven recommendations and improve data quality.

Support ESG Data Management by validating energy invoices, managing retailer relationships, maintaining sustainability data and reporting, monitoring and optimising operational energy, and producing financial reports, accruals, budgeting and forecasting to inform clients' net-zero and resilience planning.

Validate invoices and manage energy retailers; maintain sustainability data and reporting; monitor and optimise operational energy; prepare tailored financial reports, accruals, budgeting and forecasting; engage stakeholders and deliver data-driven insights using Excel.

Provide sales team administration across quote-to-cash activities: generate quotes and purchase orders, validate pricing and terms, maintain vendor relationships and pricing, capture data, produce reports, file and manage sales documentation, and recommend process improvements.

Partner with product, operations, and stakeholders to build and maintain dashboards (SQL, Tableau), investigate data to explain trends, design reports, enable self-service analytics, and ensure analyses comply with privacy and security policies to drive business decisions.

Intelligen is seeking Data Engineers with 3-5+ years of experience in ETL and DWH technologies, particularly with Snowflake and dbt, to improve client data models and pipelines.

The Engineering Lead will oversee the APAC Data Engineering Platform team, responsible for developing a scalable data platform, leading team development, collaborating with stakeholders, and ensuring high engineering standards.

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 product strategy and delivery for Fund Accounting and Administration data and reporting across APAC. Define data models, distribution strategies, and client reporting experience. Drive AI-enabled reporting platforms, data governance, and cross-functional initiatives to improve accuracy, automation, and client self-service while managing vendor relationships and risk.

Build and productionize ML systems for personalization, search, and recommendations. Apply statistical and ML models using Python and SQL, collaborate with stakeholders, and measure business impact across cloud environments.

Analyze large datasets to identify trends and deliver actionable insights; design and monitor KPIs; build and maintain dashboards and reporting frameworks; conduct ad hoc analyses; ensure data quality and support reporting infrastructure in partnership with product and business teams.