2 months ago
Canberra, AustraliaSenior
Responsibilities
- Lead the design and evolution of enterprise data warehouse, lakehouse, and analytics architectures across cloud, on-premises, and SaaS environments.
- Define and implement conceptual, logical, physical, and dimensional data models, architecture standards, integration patterns, and governance frameworks.
- Provide technical leadership across ETL/ELT development, data integration, platform modernisation, and cloud transformation initiatives.
- Partner with engineers, analysts, architects, and business stakeholders to deliver secure, scalable, maintainable, and high-performing data solutions.
- Optimise data platform performance, scalability, storage, workload management, and operational reliability.
- Provide technical leadership, mentoring, and architectural guidance while explaining complex technical concepts to varied audiences.
Requirements
- Significant experience in data warehousing, data architecture, database design, or data engineering within complex enterprise environments.
- Experience designing conceptual, logical, physical, and dimensional data models, including star schemas, fact tables, and dimension tables.
- Advanced SQL capability and experience with relational database technologies and large-scale data environments.
- Experience designing and overseeing ETL/ELT pipelines using Azure Data Factory, AWS Glue, Informatica, dbt, SSIS, or similar platforms.
- Knowledge of modern cloud data platforms such as Snowflake, Databricks, Microsoft Fabric, Amazon Redshift, Google BigQuery, or Azure Synapse.
- Experience with data governance, metadata management, lineage, security, privacy, and data quality frameworks.
- Experience collaborating with business and technology stakeholders and providing technical leadership, mentoring, and architectural guidance.
- Experience with cloud-native data architectures, data lakes, lakehouses, and modern analytics platforms is advantageous.
- Knowledge of Databricks, Delta Lake, Spark, Kafka, or other modern data processing and streaming technologies is advantageous.
- Experience with dbt and contemporary data transformation practices is advantageous.
- Familiarity with Data Vault, Kimball, data mesh, or other enterprise data modelling and architecture approaches is advantageous.
- Experience implementing CI/CD practices and Infrastructure as Code within data platforms is advantageous.
- Exposure to data cataloguing, governance, or metadata management tools is advantageous.
- Relevant qualifications or certifications in cloud architecture, data engineering, database technologies, or enterprise architecture are advantageous.
- The successful applicant must be eligible to obtain and maintain an NV1 Security Clearance.
Benefits
- Flexible work options including Life Days of up to 12 extra days off per year, part-time arrangements, job sharing, and compressed hours.
- Free access to Headspace and MeQuilibrium, plus dedicated wellness spaces.
- Access to more than 120,000 on-demand courses and technical, project management, leadership, and professional development opportunities.
- Career development support for deepening technical expertise or preparing for leadership.
- Opportunity to contribute to nationally significant government and digital modernisation projects.
Tech Stack
Categories
Data Engineering
About Leidos
Leidos builds and operates technology and engineering solutions for defense, intelligence, civil, and health agencies, including systems integration, cyber, enterprise IT, sensors, and healthcare IT. It primarily sells through government contracts and long-term programs, plus managed services and mission software. Founded in 1969 and headquartered in Reston, Virginia, Leidos is a Fortune 500 public company on the NYSE; customers include U.S. defense and intelligence agencies and civil bodies such as the FAA.