TPG Telecom started with a belief that we can make things better for Australians. We believe in the power of meaningful relationships to support vibrant, connected communities where everyone belongs. It’s our why; our reason to exist. Now, we’re proud to be one of Australia’s leading mobile and internet providers. We’re not just redefining telco, we’re changing its course. And you play a role in that. A big one. The opportunities are plenty for those who are ready to accept the challenge.
Could that be you?
Your opportunity:
This is an important role to enable the execution of our already defined AI Strategy. As a Senior Data Engineer in the Data and Analytics Centre of Excellence (CoE), reporting to the Head of Data Engineering, you will build pipelines and processes to transform data from foundational data products to derived data products on a modern data and AI platform. The objective of these data products is to create highly reusable and ready-to-consume datasets that ensure a single source of truth and enable flexible and rapid use of data and AI/ML customer centric use cases.
You will partner closely with Technology teams, especially Cloud Engineering, Software Engineering, and Architecture teams, as well as the Networks and Tech Ops teams across the Technology function (OneTech). You will also partner with other peers in the CoE – especially Data Governance, Data Science, Analytics Products, Business Partnering – to develop scalable solutions, including continuous monitoring and optimisation of data platforms and pipelines.
About You
You are a customer centric and self-starter engineer. You critically think about your work and how you will deliver it. As a resourceful and collaborative team player, you enjoy ambitious goals and taking on challenging work that’s not been fully figured out yet. You think outside the box and always looking for a better way of delivering great work. Your ambition is to exceed expectations in whatever you take on.
You'll make impact by:
Engineering
Day-to-day execution of the data and AI product roadmap and strategy.
Build high-quality ETL/ELT and MLOps data ingestion and transformation pipelines, patterns and practices to deliver a single source of truth platform of reusable data and AI products.
Script, support and maintain quality source code. Code review other engineer’s source code.
Build, support, maintain and document a high-performing data engineering, machine learning operations and AI platform.
Implement data and AI engineering continuous improvement opportunities and best practices.
Support the infrastructure teams to ensure end-to-end delivery of data engineering, machine learning operations and AI services.
Troubleshoot, resolve and document data and AI engineering pipeline issues as they arise.
Support business as usual (BAU) data products and platforms to meet prescribed Service Level Agreements (SLA).
Collaboration
Adopt and embrace agile ways-of-working and scrum cadence.
Knowledge share with other engineering and IT teams to ensure the delivery of reusable data products to the business.
Support the Governance & Risk teams to ensure we are effectively managing the risk of our data products.
Partner closely with peers and stakeholders: Data Engineers, ML Engineers, Data Scientists, Analysts, Business Partners, Platform/DevOps Engineers, Project Managers/Scrum Masters, Testers and other business stakeholders as required.
Support the overall Data & Analytics Centre of Excellence strategy and its execution.
Collaborate to create outstanding people experiences for the data and AI teams
What you’ll bring:
Experience working in highly matrixed and established organisations (e.g. telco, finance, insurance or retail)
University degree in a quantitative discipline, such as computer science, engineering, mathematics, or equivalent experience.
At least 5 years of hands-on data engineering or machine learning ops experience.
Advanced SQL and proficient Python knowledge.
Hands-on ETL/ELT and MLOps platforms with dimension modelling and feature engineering experience (e.g. Data Lakes, Data Warehousing, Star Schema, Change Data Capture, Orchestration, PII protection, Observability).
Experience with AI platforms, patterns, tooling or proof-of-concept solutions: Integration and Gateways, Orchestration and Agentic AI, Foundation Models, Model Hubs and Augmentation, Security and Responsible AI.
Experience with one or more of the major cloud providers (e.g. AWS, GCP, Azure).
Experience with a modern data stack, particularly dbt, Databricks, Redshift, Airflow.
Ideally, you will also have:
Ideal, but not required: Software engineering, DevOps experience (e.g. CICD, Source Control, Java, Terraform).
A “solutions mindset” of natural curiosity and a strong motivation to solve problems, no matter how complicated or ambiguous they might be.
Above all else, a “can-do” attitude is the most important qualification!
Benefits
Flexible hybrid way of working (from home and office)
‘Stay Connected Mobile’ – Access to a free mobile plan
‘Stay Connected NBN’ – Access to a free, high‑speed NBN plan (up to 500 Mbps)
‘Your Leave’ - additional leave to be used whenever you like - every year
Access to TPG Learning Hub platform and internal development opportunities
Access to Corporate Partner Discounts
Don’t meet every single requirement? That’s OK! At TPG Telecom, we’re all about creating an accessible workplace where everybody feels safe to bring their authentic self to work - regardless of background. If you think this role is a great fit for you but some of the qualifications don’t align with your experience, we still encourage you to apply - you might just be the perfect candidate for a similar role with us! Learn more about life at TPG Telecom here.
TPG Telecom also acknowledges the Gadigal People of the Eora Nation as the Traditional Custodians of lands and waterways where this office can be found in Barangaroo.
Our Talent Acquisition Team and Hiring Managers kindly request no unsolicited resumes or approaches from Recruitment Agencies. TPG Telecom is not responsible for any fees related to unsolicited resumes.
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