Top Data Jobs in Sydney

Work directly with B2B SaaS customers to onboard, structure, and improve product data. Build simple POCs, drive adoption and satisfaction, support renewals, identify expansion, and feed customer insights to product and engineering.

Lead the Human Data function to deliver high-quality training data and evaluations. Manage Team Leads and AI Tutors, define annotation guidelines and QA processes, scale operations globally, recruit and develop talent, collaborate with engineering and program teams, and report outcomes to senior leadership.

Partner with ANZ Strategy & Operations and executives to analyze marketplace dynamics across consumers, merchants, and Dashers. Build experiments, reports, dashboards, and statistical analyses (SQL, Python/R, regression, A/B testing) to surface insights, measure levers, and drive data-informed strategic recommendations and initiatives.

As a Software Engineer in Data, you will design, build, and support data systems, create real-time data pipelines, and manage data workflows, collaborating across teams.

Lead growth and market strategy for data centre opportunities across Australia and New Zealand. Identify and qualify large-scale pursuits, drive win themes and pricing, engage developers/operators/EPCs, coordinate cross-functional pursuit teams, and ensure smooth handover to execution while capturing market insights and building partner relationships.

Abstract and enter clinical data from health records, ensure data quality and confidentiality, use EHR/practice management systems and Microsoft Office, troubleshoot basic access/VPN/MFA issues, and collaborate with clinical teams to support operations.

Lead advanced analytics and ML projects: perform EDA, feature engineering, statistical modeling, and build/optimize production ML and Generative AI solutions (LLMs, embeddings, RAG). Collaborate with engineering and MLOps to deploy and monitor models, create visualizations and dashboards, translate insights for stakeholders, and mentor junior team members.

The Senior Data Scientist will tackle business challenges using advanced analytics and machine learning, developing models and solutions collaboratively within teams while ensuring timely project delivery and documentation.

Lead administration and development of Salesforce NPSP, Marketing Cloud and related supporter systems. Build automations and integrations, manage data governance, support campaign delivery, train users, and improve system reliability and usability.

Lead design, build and optimise enterprise data platform and pipelines (Microsoft Fabric migration, AX ERP modelling, Denodo). Ensure platform performance, governance, incident management, and mentor junior/offshore engineers while driving stakeholder delivery in an agile environment.

Design and implement enterprise data governance frameworks, policies, and operating models. Establish stewardship, metadata management, lineage, and data quality practices. Facilitate stakeholder workshops, embed governance into delivery, support governance tooling adoption (data catalogs, metadata platforms), and provide advisory and mentoring to uplift organisational governance capability.

Lead and build Audinate's Data and Insights function: design data warehouse, modelling layer, and reporting; partner with business leaders to surface insights that drive growth; establish governance, self-service analytics, and a small product-minded analytics team.

Lead marketing analytics for a high-growth SaaS company: build attribution frameworks, develop channel and program metrics, create dbt data models, deliver dashboards and deep-dive analyses, and connect marketing investment to pipeline, ARR, and payback for stakeholders across marketing, finance, and executives.

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 Document Controller coordinates document management across Design Engineering and Construction projects, ensuring accessibility, compliance, and effective communication among teams and stakeholders.

Research and verify contact information for evangelical megachurches across the U.S.; collect and maintain structured databases in Excel/Sheets/CRM; identify decision-makers; ensure data accuracy and formatting; remove duplicates; categorize records; produce progress reports and support outreach preparation.

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.

Deliver and own operational reporting, dashboards and ad-hoc analysis using governed data. Partner with stakeholders to translate business questions into insights, ensure data quality, documentation, and apply data governance in an Agile environment.

The role involves strategic data leadership, defining measurement strategies for productivity, leading experimentation, coaching teams, and identifying growth opportunities in product development.

The Data Scientist will analyze data to guide decision-making, create experiments, and collaborate with teams at DoorDash to optimize operations and drive business insights.

Design, build and operate scalable Lakehouse data platform and batch/real-time pipelines. Develop data assets, streaming solutions (Kafka/Flink), ETL/ELT workflows (Airflow, DBT), and cloud-native architectures on AWS, Snowflake and Databricks. Optimize SQL and Spark workloads, manage schema and performance for Iceberg tables, enforce data quality and governance, and partner with data science, analytics and product teams to deliver cost-efficient, reliable data solutions.

Maintain and build SQL-based reports and dashboards, debug data pipelines, validate joins and business logic, own recurring reporting cycles, develop dbt models and documentation, handle stakeholder requests, and support exploratory analyses and analytics engineering tasks.

The Sr Data Engineer will optimize and refactor data pipelines, manage data ingestion from APIs, and support product engineering teams. Responsibilities include design best practices, enhance the modern data stack, and troubleshoot data issues, ensuring resilience and operational excellence.

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 a backend data platform team to build scalable ingestion, enrichment, and processing pipelines (including video transcription), own integrations with tools like Gong and Zendesk, coach engineers, run team rituals, and design event-driven architectures with monitoring and observability.