Tiimely logo

Data Engineer

Tiimely
Department:Design
Type:HYBRID
Region:Adelaide
Location:Adelaide, South Australia, Australia
Experience:Mid-Senior level
Estimated Salary:A$110,000 - A$140,000
Skills:
SQLPYTHONDATA MODELINGETLELTLAKEHOUSE ARCHITECTUREAPACHE ICEBERGDBTAPACHE AIRFLOWAGILEVERSION CONTROLCI/CDDATA GOVERNANCEPII HANDLINGAWSPOWER BITABLEAUDATA OBSERVABILITYREAL-TIME PROCESSINGAI TOOLSFINTECH
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Job Description

Posted on: March 14, 2026

About Us

Tiimely is a platform technology company leading the future of financial assessment through predictive and explainable AI, and configurable, API-led solutions. Our proprietary technology automates financial assessment and credit decisioning, enabling faster, more accurate outcomes across a range of use cases, and is licensed by large banks, fintechs, ASX-listed brands and our own in-house retail business, Tiimely Home.

Headquartered in Adelaide with a national team of 130, we’re on a mission to transform how Australians access credit through cutting-edge technology and a human-first approach. As a certified B Corp, our values — Time to be Human, Time to Take Responsibility, Time to be Transparent, and Time to Build Good Bonds — shape how we build products and how we work together.

The Position

We are looking for a Data Engineer who can independently build and improve the trusted data platform capabilities that support analytics, reporting, data science, and decision-making across our business. As a Data Engineer at Tiimely, you’ll design, build, and operate data pipelines, curated data assets, and platform capabilities that support analytics, reporting, and downstream data consumption. You’ll contribute to the evolution of a modern open lakehouse environment and help ensure data products are reliable, well-governed, and aligned with business, operational, and regulatory needs.

You’ll work closely with data specialists, software engineers, business stakeholders, and analytics consumers to translate requirements into trusted and usable data outcomes. Our platform processes real credit applications in a regulated financial services context, understanding how your data assets support lending workflows, compliance requirements, and business decision-making is part of what makes this role distinctive.

Your responsibilities will include:

  • Design, build, and maintain scalable pipelines that ingest, process, transform, and deliver data from a range of internal and external source systems.
  • Develop curated datasets and data models to support analytics, reporting, and downstream consumption.
  • Implement and maintain transformation workflows across batch and platform-driven processing patterns.
  • Contribute to the development of Tiimely’s open lakehouse architecture, including medallion-aligned data layers and open table formats such as Apache Iceberg.
  • Monitor pipeline health, investigate data issues, and improve reliability, performance, and recoverability across the platform.
  • Embed data quality controls, validation, reconciliation, and observability throughout the data lifecycle.
  • Apply sound data modelling practices, including dimensional modelling and analytical data structures.
  • Contribute to schema management, metadata quality, lineage awareness, and controlled access to data assets.
  • Work with sensitive data in accordance with governance requirements, including PII handling, de-identification, and data retention obligations.
  • Partner with data scientists, BI analysts, software engineers, and business stakeholders to deliver trusted data products.
  • Participate in design discussions, code reviews, and continuous improvement initiatives relating to the data platform.
  • Stay current with evolving data engineering practices, tools, and technologies, and share knowledge with peers.

What We’re Looking For

Required

  • Demonstrated experience in a Data Engineering or similar mid-level role.
  • Solid SQL and Python capability.
  • Sound understanding of data modelling, including dimensional modelling and analytical data structures.
  • Good understanding of ETL and ELT patterns and the practical trade-offs of each.
  • Familiarity with modern data platform concepts, including lakehouse architecture and open table formats such as Apache Iceberg.
  • Experience with data engineering tooling including dbt and workflow orchestration platforms such as Apache Airflow.
  • Experience working in Agile development environments.
  • Familiarity with version control, testing, CI/CD, and collaborative engineering practices.
  • Awareness of data governance concepts including PII
  • handling, de-identification, retention, and secure use of regulated data.
  • Solid analytical thinking, debugging, and problem-solving capability.
  • Clear written and verbal communication skills, including the ability to collaborate effectively with technical and non-technical stakeholders.

Desirable

  • Experience with AWS data services or other cloud-based data platforms.
  • Exposure to BI tools such as Power BI or Tableau.
  • Familiarity with data observability, lineage, and platform monitoring practices.
  • Exposure to real-time or near real-time data processing patterns.
  • Experience leveraging AI tools and technologies to enhance data engineering productivity or problem-solving.
  • Experience in fintech, financial services, or another regulated environment.

What’s In It For You?

  • Competitive remuneration package with employee share plans that reward individual and company success.
  • Flexible working arrangements, including hybrid options.
  • Ongoing learning and development to support your professional growth.
  • A modern office environment designed for a scaling fintech.
  • A supportive and inclusive team culture backed by our B Corp certification and values-driven approach.

If this sounds like you, we’d love to hear from you. Even if you don’t tick all the boxes, we encourage you to apply.

If you have any questions or need adjustments to our hiring process, please get in touch with Karam Singh at karam.singh@tiimely.com.

Originally posted on LinkedIn

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