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
- Manager of Data Engineering developing and optimizing data platforms for enterprise analytics. Leading a team and collaborating with business stakeholders for high-quality data solutions.
Responsibilities
- Lead, mentor, and grow a team of Data Engineers, including recruitment, onboarding, performance management, and continuous skill development.
- Oversee the design, development, and optimization of data pipelines, architectures, and data sets for batch and streaming use cases.
- Collaborate with business units and analysts to gather requirements and translate them into scalable data solutions.
- Drive best practices in data modeling (conceptual, logical, and physical) to support analytics and reporting.
- Establish and maintain metadata standards, data cataloging, and lineage to improve data discovery and governance.
- Ensure timely delivery of projects by managing priorities, scope, and resources in an Agile framework.
- Partner with vendors and managed service providers as required to support delivery and operations.
- Define, track, and report on team KPIs such as data pipeline uptime, data freshness, and adoption metrics.
- Contribute to capacity planning, cloud cost optimization, and vendor performance review.
- Promote standardization and reusability of data models, pipelines, and integration approaches.
- Serve as a subject matter expert, communicating technical solutions to both technical and non-technical audiences.
- Maintain compliance with organizational standards for security, architecture, and development practices.
- Encourage experimentation with automation, AI/ML integration, and modern data engineering practices.
- Provide input into enterprise architecture and align data initiatives.
Requirements
- Bachelor’s degree in computer science, Engineering, or related technical discipline.
- 5+ years of experience in data engineering, including 2–3 years in a leadership or managerial role.
- Proficiency in Python, SQL, and Spark (batch and streaming).
- Experience developing and maintaining data lakes and cloud-based platforms (preferably Azure/Databricks).
- Strong background in data architecture, including data modeling (conceptual, logical, dimensional) and relational databases.
- Familiarity with data mesh or federated architectures for distributed data ownership.
- Demonstrated ability to quantify business impact of data initiatives (e.g., cost savings, reduced downtime, increased adoption).
- Knowledge of data governance, data quality, and master data management practices.
- Proven ability to mentor and develop technical talent.
- Hands-on experience with Agile/Scrum methodologies.
- Excellent communication, problem-solving, and collaboration skills with a strong delivery-focused mindset.
- Knowledge of orchestration tools (SSIS, ADF, DBT or equivalent).
- Familiarity with DevOps/DataOps practices—CI/CD, version control, automated testing for data pipelines.
- Exposure to streaming technologies like Kafka/Event Hubs beyond Spark Streaming.
- Understanding of security and compliance frameworks (PII, GDPR, SOC2, HIPAA depending on industry).
Benefits
- Job Benefits
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