Technical Lead for data engineering and reporting in healthcare technology at Dedalus. Shaping innovative software solutions and leading cross - functional technical teams in Australia.
About the role
- Senior Data Engineer designing and optimizing data assets and pipelines for General Motors. Collaborating with teams to improve operational performance and analytics capabilities.
Responsibilities
- Assemble large, complex data sets that meet functional and non-functional business requirements.
- Identify, design, and implement process improvements, including automation, data delivery optimization, and infrastructure redesign for scalability.
- Lead and deliver data-driven solutions across multiple languages, tools, and technologies.
- Contribute to architecture discussions, solution design, and strategic technology adoption.
- Build and optimize highly scalable data pipelines incorporating complex transformations and efficient code.
- Design and develop new source system integrations from varied formats (files, database extracts, APIs).
- Design and implement solutions for delivering data that meets SLA requirements.
- Work with operations teams to resolve production issues related to the platform.
- Apply best practices such as Agile methodologies, design thinking, and continuous deployment.
- Develop tooling and automation to make deployments and production monitoring more repeatable.
- Collaborate with business and technology partners, providing leadership, best practices, and coaching.
- Mentor peers and junior engineers; educate colleagues on emerging industry trends and technologies.
Requirements
- Bachelor’s degree in Computer Science, Software Engineering, or related field, or equivalent experience
- 7+ years of data engineering/development experience, including Python or Scala, SQL, and relational/non-relational data storage. (ETL frameworks, big data processing, NoSQL)
- 3+ years of experience in distributed data processing (Spark) and container orchestration (Kubernetes)
- Proficiency in data streaming in Kubernetes and Kafka
- Experience with cloud platforms – Azure preferred; AWS or GCP also considered.
- Solid understanding of CI/CD principles and tools
- Familiarity with big data technologies such as Hadoop, Hive, HBase, Object Storage (ADLS/S3), Event Queues.
- Strong understanding of performance optimization techniques such as partitioning, clustering, and caching
- Proficiency with SQL, key-value datastores, and document stores
- Familiarity with data architecture and modeling concepts to support efficient data consumption
- Strong collaboration and communication skills; ability to work across multiple teams and disciplines.
Benefits
- medical
- dental
- vision
- Health Savings Account
- Flexible Spending Accounts
- retirement savings plan
- sickness and accident benefits
- life insurance
- paid vacation & holidays
- tuition assistance programs
- employee assistance program
- GM vehicle discounts and more.
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