Finstro is a commercial non-bank lender growing fast — and the data platform is growing with it. We're in the middle of a meaningful rebuild: a full star schema / medallion architecture on AWS Redshift, designed from the ground up as a core data asset the business can actually rely on. The foundation is being laid now, and there's a long runway of additional assets to build using the same methodology, plus new data sources coming into the lake.

This is a hands-on engineering role for someone who wants to own real work, not maintain someone else's. You'll be close to the business translating requirements into technical solutions, designing and optimising data models in Redshift, and building ETL pipelines in AWS Glue. Strong analytical instincts matter here as much as technical ability; you'll need to ask the right questions before you write the first line of code.

If you've got solid data engineering fundamentals, genuine AWS depth, and you thrive when the answer isn't obvious yet this is worth a conversation.

Joining an established data engineering function, this role focuses on new build delivering additional data assets on the existing architectural foundation while the platform scales. You'll work closely with the existing engineer, who provides technical direction and sign-off, giving you a strong peer to work with as you take ownership of greenfield development.

• Act as the internal subject matter expert for data engineering providing technical guidance to analytics, Head of Data and the broader technology team, contributing to architectural decisions, and helping shape how data engineering is practised at Finstro as the platform and team mature.

• Engage with business stakeholders to understand data needs and translate them into well-defined specifications, ensuring the technical solution reflects the intent, not just the ask.

• Design, build, and maintain ETL pipelines in AWS Glue, from ingestion through to clean, reliable data in the lake and Redshift with an eye on performance, cost, and maintainability from the start.

• Apply and help evolve the team's data modelling standards including star schema design and medallion architecture principles ensuring consistency across assets as the platform grows.

• Perform data modelling, schema design, and optimization in Redshift to ensure efficient data storage and retrieval.

• Document data models, pipeline logic, and lineage to ensure the platform is understandable and maintainable as it scales.

• Write clean, well-tested, and version-controlled code; participate in code reviews and contribute to raising the engineering bar across the team.

• Collaborate with data scientists, analysts, and other stakeholders to ensure data quality and integrity.

• Develop and enforce best practices for data management, including data security, data governance, and data quality standards.

• Own the reliability of data pipelines end-to-end proactively monitoring for failures, investigating root causes, and resolving issues before they impact downstream consumers.

• Communicate clearly with stakeholders on data availability, pipeline status, and any issues affecting delivery managing expectations proactively rather than reactively.

• Continuously evaluate and integrate new technologies and tools to enhance the data platform.

• Contribute to broader data team initiatives as the platform and team evolve.

Position Requirements

• Bachelor’s degree in computer science, Information Systems, or a related field or equivalent experience.

• 3+ years in data engineering or a similar role, with experience in a fast-paced or regulated environment.

• Strong proficiency in AWS Glue for ETL processes, including writing and debugging ETL scripts.

• Extensive experience with AWS Redshift, including data modelling, schema design, and query optimization.

• Proficiency in using AWS Data Lake, S3 for data storage and management.

• Experience with dbt and Terraform or similar

• Strong SQL skills, with the ability to write complex queries and optimize performance.

• Deep practical experience with data warehousing concepts, including star schema and snowflake schema design.

• Knowledge of Python for data processing and automation.

• Familiarity with version control systems, such as Git.

• Experience with data governance, data quality, and data security best practices.

Preferred Skills

• Experience with other AWS services such as Lambda, IAM.

• Experience with Dev-Ops process and AWS CI/CD pipelines.

• Experience with Agile methodologies and tools, such as JIRA, confluence.

• Certification in AWS (e.g., AWS Certified Data Analytics) is desirable.

Location

This role is based in Sydney, Australia. Candidates must be in and authorised to work in Australia.

*Due to the high volume of applications we receive, only shortlisted candidates will be contacted. We appreciate the time and interest of all applicants; however, we may not be able to respond individually to every candidate.