At a Glance
- Tasks: Lead and scale a cutting-edge data platform in a fast-growing InsurTech company.
- Company: Join one of Australia's fastest-growing insurance and health platforms.
- Benefits: Competitive salary, meaningful equity, hybrid work, and unlimited AI tools.
- Other info: Enjoy a collaborative culture with office dogs, snacks, and exciting company retreats.
- Why this job: Shape the future of healthcare and insurance through innovative data and AI solutions.
- Qualifications: 5+ years in data platforms, strong Python and SQL skills, and cross-functional collaboration.
The predicted salary is between 85000 - 85000 £ per year.
Location: Sydney (Hybrid - Tue/Wed/Thu in office)
Industry: InsurTech
Salary: Up to £85,000 (AUD 160,000) + meaningful equity
The Deal: You sort your first 6 months on a working holiday visa, then they take over with full company-paid sponsorship. They handle the bit that's usually stopped you doing this for the last couple of years.
The Opportunity: I'm currently working with one of Australia's fastest-growing insurance and health platforms, who are looking to hire a Data Engineering Lead to own and scale their entire data platform. They're open to speaking with exceptional UK-based professionals who are excited by the opportunity to relocate to Sydney and be part of a high-growth, AI-driven business. This is a unique opportunity for an ambitious data leader who wants to sit at the intersection of data, AI, analytics, and platform engineering within a high-growth Series A environment. You'll play a critical role in shaping the foundations that power pricing, claims, operations, product and AI decisioning across the business. You'll work closely with engineering, product, pricing, operations and AI teams to build clean, trusted, real-time data systems that support automation, experimentation and intelligent decision-making at scale.
My client offers a collaborative, low-ego culture, unlimited AI tooling, meaningful equity upside, and the opportunity to help shape the future of healthcare and insurance through data and AI.
Key Responsibilities
- Own and scale end-to-end data platform including ingestion, pipelines, warehousing, observability and automation.
- Design and maintain real-time and batch data pipelines across pricing, claims, operations, product and AI systems.
- Build and optimise analytics-ready data models and warehouses using modern tooling such as BigQuery and dbt.
- Partner closely with AI engineers to productionise AI agents and models with clear evaluation frameworks, monitoring and feedback loops.
- Implement robust observability, testing, alerting and fail-safe systems to ensure data reliability and trustworthiness.
- Develop human-in-the-loop workflows and feedback pipelines to improve AI model performance and evaluation.
- Enable teams across the business to self-serve analytics, reporting, experimentation and operational insights.
- Support the development of scalable, secure and maintainable data architecture in a fast-moving environment.
Essential Skills & Experience
- 5+ years' experience building and maintaining data platforms or analytics engineering stacks at scale.
- Strong experience with Python and SQL in production data environments.
- Hands-on experience with dbt, modern cloud data warehouses, and event-driven architectures.
- Experience designing reliable batch and/or streaming pipelines with strong observability and testing practices.
- Strong understanding of data modelling, schema design, data contracts and analytics engineering best practices.
- Ability to work cross-functionally with engineering, product, operations and leadership stakeholders.
- Strong communication skills and a proactive, ownership-driven mindset.
Desirable Skills
- Experience designing evaluation frameworks for AI/LLM systems including offline evaluations, regression testing and monitoring.
- Experience supporting AI agents, ML products or feedback-loop systems.
- Exposure to GCP/BigQuery, Typescript and modern event infrastructure.
- Background within insurance, healthcare, fintech or other regulated environments.
- Experience building real-time operational or pricing systems.
Package & Benefits
- Up to £85,000 (AUD 160,000) salary + meaningful equity package
- Hybrid working - 3 days per week in the Sydney office
- Unlimited AI tooling with no token limits or approval processes
- Latest MacBook Pro and premium working setup
- Two company retreats per year
- Collaborative, high-performing engineering culture
- Office dogs, unlimited snacks and excellent office environment
- Opportunity to shape data and AI strategy within a rapidly scaling business
Lead Data Engineer in England employer: Oscar
Join one of Australia's fastest-growing InsurTech companies as a Lead Data Engineer in Sydney, where you'll thrive in a collaborative, low-ego culture that champions innovation and employee growth. With a competitive salary, meaningful equity, and the chance to work with cutting-edge AI tools, this role offers a unique opportunity to shape the future of healthcare and insurance while enjoying a hybrid work model and a vibrant office environment. Experience the excitement of being part of a high-growth Series A business that values your contributions and supports your professional development.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Engineer in England
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working in Australia. Use LinkedIn to connect and engage with them; you never know who might have a lead on that perfect Data Engineering Lead role.
✨Tip Number 2
Prepare for interviews by brushing up on your technical skills and understanding the company’s data platform. Be ready to discuss how you can scale their data systems and improve AI decision-making. Show them you’re the right fit for their collaborative culture!
✨Tip Number 3
Don’t just apply anywhere; focus on companies that excite you! Check out our website for opportunities that match your skills and aspirations. Tailor your approach to highlight how you can contribute to their data-driven goals.
✨Tip Number 4
Follow up after interviews! A quick thank-you note can go a long way. Reiterate your enthusiasm for the role and mention something specific from the conversation that resonated with you. It keeps you fresh in their minds!
We think you need these skills to ace Lead Data Engineer in England
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Lead Data Engineer role. Highlight your experience with data platforms, Python, SQL, and any relevant projects that showcase your skills in AI and analytics. We want to see how you can bring value to our team!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're excited about the opportunity to work with us in Sydney. Share your passion for data engineering and how you envision contributing to our mission in the InsurTech space.
Showcase Your Projects:If you've worked on any interesting data projects, make sure to mention them! Whether it's building data pipelines or optimising data models, we love seeing real-world applications of your skills. It helps us understand your hands-on experience.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. Plus, it shows you're keen to join our team!
How to prepare for a job interview at Oscar
✨Know Your Data Inside Out
Make sure you’re well-versed in the specifics of data platforms and analytics engineering. Brush up on your experience with Python, SQL, and tools like dbt and BigQuery. Be ready to discuss how you've built and maintained data pipelines in previous roles.
✨Showcase Your Cross-Functional Skills
This role requires collaboration with various teams, so highlight your ability to work with engineering, product, and operations. Prepare examples of how you’ve successfully partnered with different stakeholders to achieve common goals.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions about data modelling, schema design, and observability practices. Brush up on your knowledge of event-driven architectures and be ready to explain your approach to building reliable data systems.
✨Demonstrate Your AI Knowledge
Since this position involves working closely with AI engineers, be prepared to discuss your understanding of AI evaluation frameworks and feedback loops. Share any relevant experiences you have with AI agents or ML products to show your expertise in this area.