Top Data Engineer Jobs in Sydney

Maintain Cloudflare’s global data center network and operational availability across remote colocation facilities. Responsibilities include configuring network infrastructure, coordinating contractors and vendors, managing deployments and decommissions, creating SOPs and operational documentation, improving processes through automation and AI tools, supporting troubleshooting and root-cause analysis, and participating in a 24/7 on-call rotation. The role also involves basic program management, cross-functional collaboration, and limited travel.

Designs, builds, optimizes, and supports scalable batch and streaming data pipelines and lakehouse solutions using Azure Databricks, PySpark, Python, SQL, and Delta Lake. Integrates Azure data services, develops production workflows, implements CI/CD, security, governance, monitoring, and data-quality controls, and tunes Spark workloads. Partners with cross-functional stakeholders to deliver reliable data products while providing technical leadership, code reviews, mentoring, and production support.

Designs, builds, optimizes, and supports scalable batch and streaming data pipelines and lakehouse solutions using Azure Databricks and related Azure services. Develops ingestion, transformation, validation, and publishing frameworks; implements Delta Lake and medallion architectures; optimizes Spark workloads; builds CI/CD processes; applies security and governance controls; monitors production systems; and provides technical leadership, code reviews, and mentoring.

Build and maintain secure, scalable real-time and batch data pipelines on Google Cloud. Develop transformation workflows, orchestrate cloud services, integrate upstream data sources, troubleshoot ingestion issues, and implement governance controls including data masking, redaction, row-level and column-level security, taxonomies, and policy tags.

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 optimize scalable ETL/ELT pipelines using Azure Databricks, SQL, and Python. Develop data ingestion processes, API integrations, and automated data solutions; troubleshoot pipeline, data quality, integrity, and performance issues; and support production monitoring. Collaborate with technical and business stakeholders to deliver reliable data for reporting and analytics, while documenting architecture, transformations, and data flows.

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.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Build and maintain data ingestion pipelines and data products using AWS services. Collaborate with business data owners to curate core data assets, create reports, dashboards, files, and APIs, and establish data engineering standards. Design scalable, automated pipelines for critical market infrastructure, using streaming, data modeling, DevSecOps, and infrastructure-as-code practices. Support centralized data platforms, data governance, and domain-aligned data products.

Design, build, and optimize scalable cloud data platforms, pipelines, data models, lakehouses, and warehouses using Microsoft Azure and Fabric. Integrate cloud and on-premises sources, develop transformations with SQL, Spark, and Python, and implement governance, security, quality, CI/CD, and performance controls. Collaborate with architects, analysts, data scientists, and stakeholders to support analytics, reporting, AI, and machine learning initiatives.

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.

Builds and supports enterprise data solutions using Microsoft Fabric and Power BI. Responsibilities include developing batch and near-real-time pipelines, transforming data with SQL, Python, and PySpark, implementing medallion architecture, modeling data, supporting reporting, and ensuring security, governance, quality, and reliability. The role also involves troubleshooting, documentation, platform modernization, stakeholder collaboration, and operational support.

Designs and maintains scalable data pipelines and models, centralizes enterprise data in a warehouse, and processes hundreds of millions of daily events. Leads technical solution design, promotes coding, DevOps, metadata, and testing standards, and integrates AI tools into engineering workflows. Collaborates with Product, Cyber Security, Platform Engineering, and stakeholders to deliver data publishing solutions, resolve incidents, document systems, and shape requirements.

Leads mechanical design and engineering for data centre projects in Sydney, ensuring performance, energy efficiency, quality, schedule, and budget objectives. Collaborates with multidisciplinary teams, engages clients, manages stakeholders, mentors junior engineers, and incorporates emerging industry solutions. Requires data centre mechanical engineering experience, project leadership capability, and a relevant engineering degree.

Lead the design, development, optimization, and support of enterprise data platforms using Microsoft Fabric. Build scalable batch and near-real-time pipelines, lakehouses, warehouses, semantic models, and data transformations. Drive governance, security, performance, disaster recovery, and platform modernization while supporting production environments. Provide technical leadership, mentoring, stakeholder engagement, troubleshooting, and continuous improvement across enterprise data and analytics initiatives.

Operate, monitor, maintain, and improve MPLS, data center, and global WAN networks. Configure and deploy carrier-grade infrastructure, troubleshoot outages and escalated incidents, support automation using Python and CI/CD, and contribute to network design, documentation, risk management, and project delivery. The role includes Linux administration, stakeholder collaboration, and participation in an after-hours on-call roster.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Design and evolve scalable, fault-tolerant ETL/ELT pipelines and real-time data systems for financial analytics. Lead Kafka and RabbitMQ stream processing, optimize ClickHouse schemas and queries, deploy secure cloud-based data services, and implement monitoring and automated recovery. Collaborate with leadership, support data governance, mentor engineers through technical guidance and code reviews, and independently develop and optimize Python and SQL pipelines handling high-throughput market and transactional data.

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.

Leads technical pre-sales for enterprise data management solutions, including discovery, solution design, product demonstrations, proof-of-concept engagements, executive briefings, RFP responses, and partner enablement. Translates customer requirements involving cloud infrastructure, data security, governance, privacy, and compliance into measurable business outcomes. Collaborates with sales, product, engineering, marketing, customer success, and channel partners throughout complex enterprise sales cycles.

Operate and support data platforms by monitoring pipelines and ETL processes, investigating data and reporting issues, responding to alerts, troubleshooting incidents, and improving monitoring, runbooks, and automation. The role uses SQL and supports cloud, Kubernetes, legacy platforms, infrastructure-as-code, CI/CD, and Confluent event streaming. After training, the engineer will participate in a 24/7 on-call roster.

Designs and leads enterprise data platforms, lakehouse architectures, pipelines, and integration solutions. Provides technical leadership, establishes engineering standards, reviews solutions, and improves platform scalability, reliability, security, governance, and observability. Leads, coaches, and manages data engineering staff while partnering with analytics, AI, technology, and business teams to deliver trusted data services across multiple systems and domains.

Develop and optimize Supabase’s Data API based on PostgREST. Responsibilities include improving the PostgreSQL driver and performance, developing features, fixing bugs, resolving incidents and escalated support issues, improving observability, and debugging integrations across the Supabase platform. The role requires strong Haskell development, PostgreSQL and SQL expertise, Linux debugging, open-source contribution, and technical user support.

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.

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.