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Senior Software Engineer (Data)

Optiver Private Jobs
CompanyOptiver Private Jobs
CategoryEngineering
LocationSydney
RemoteOn-site (inferred)
EmploymentNot stated
LevelSenior
SalaryNot stated by the employer
Posted22 Jul 2026
Last verified30 Jul 2026
SourceEmployer career page (greenhouse)
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Description
WHO WE ARE Optiver is a global market maker founded in Amsterdam, with offices in London, Chicago, Austin, New York, Sydney, Shanghai, Hong Kong, Singapore, Taipei and Mumbai. Established in 1986, today we are a leading liquidity provider, with close to 2,000 employees in offices around the world, united in our commitment to improve the market through competitive pricing, execution and risk management. By providing liquidity on multiple exchanges across the world in various financial instruments we participate in the safeguarding of healthy and efficient markets. We provide liquidity to financial markets using our own capital, at our own risk, trading a wide range of products: listed derivatives, cash equities, ETFs, bonds and foreign currencies. Optiver’s Sydney office is one of the primary players within Asian markets, trading a range of products. Established in 1996, we're an active participant on the Hong Kong, Korea, Singapore, Taiwan and Japan exchanges, and act as Optiver’s APAC head office.   WHAT YOU'LL DO Optiver Sydney is seeking an experienced Senior Software Engineer to support and advance the data needs of our local research team. You’ll work closely with researchers in Sydney and our Global Research and Data Platform teams to productionise workflows, optimise large-scale data processing, and manage infrastructure costs efficiently. This role requires deep expertise in Software Engineering concepts and technologies - particularly in Data Engineering - along with a strong ability to work independently and deliver impactful results in a fast-paced environment. You will play a pivotal role in designing and implementing data solutions that enable efficient access to high-quality data, ensuring smooth operations of our data pipelines. Your work will directly support our research teams in deriving real-time insights and making data-driven decisions. As a key hire into the data engineering team in our Sydney office, you will partner with cross-functional teams across regions to enhance and align our data strategy, helping drive Optiver’s data and analytics capabilities forward. Dependent on experience and seniority, your responsibilities will include: Data Pipeline Design & Development: Architect, build, and manage scalable data pipelines using Spark, Databricks, and proprietary HPC tooling, ensuring high availability, scalability, and performance. Platform and Tooling Development: Advance the state of the art of Optiver’s Research and Data platforms and tools. Cost Optimisation: Monitor and optimise resource usage in partnership with the Data Platform team, balancing performance and cost-effectiveness. Monitoring & Tuning: Implement monitoring tools and fine-tune systems for optimal throughput, latency, and reliability. Cross-Functional Collaboration: Work closely with global Data Platform teams to maintain alignment on data strategy, tools, and best practices. Documentation & Standards: Develop and maintain clear documentation of data pipeline architectures, processes, and best practices. Mentorship & Guidance: Provide technical mentorship to junior engineers and support the growth of the data engineering function in Sydney.   WHO YOU ARE We are looking for a highly skilled Senior Software Engineer with: 5+ years of experience in software engineering, with a data focus. Demonstrable proficiency in Python. Familiarity with distributed data processing and building robust data pipelines. Familiarity with the use of or concepts underpinning various Data Engineering technologies and approaches. Ability to lead projects autonomously, prioritise tasks, and deliver high-quality results. Proven success working with research and platform engineering teams to support data-driven projects. Strong problem-solving skills, focusing on efficiency, scalability, and cost-effectiven
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