Job description
Our Purpose
At Xero, we’re here to help you supercharge your business. We do this by automating routine tasks, surfacing actionable insights and connecting businesses with the right data, advisors and apps. When that happens, we’re not only making life better for small business, we’ll be building a stronger economy that can change the world.
About the role
As an Analytics Engineer supporting Product Data Science, you will be responsible for delivering high-quality data assets (data pipelines and models) that enable both rapid decision-making and the development and deployment of machine learning models.
This role will play a crucial role in bridging the gap between traditional analytics engineering and the growing needs of our machine learning initiatives.
About the team
The Product Data Science & Engineering team reports into Xero’s Product function. We are responsible for empowering Xero to deliver successful customer outcomes through actionable insights and analytics.
Our team supports product teams with key information to: understand the current product landscape and user behaviour, inform and motivate product behaviour and shape product strategy.
As our function evolves, we are increasingly supporting the deployment and operationalization of machine learning models to deliver intelligent, data-driven products and services.
We do this by creating, managing, and delivering a wide range of analytical services including core reporting, key data dimensions, extensible analytical assets, and robust data pipelines for machine learning applications.
We aspire to advance a culture of data-driven decision making, made possible through high quality, timely assets, insights, and increasingly, through the delivery of ML/AI powered predictions and optimisations.
About you
We’re looking for someone who can hit the ground running, working with our counterparts in divisional pods and the applied data science teams. You’ll deliver outputs for stakeholders across Product, with an increasing focus on enabling AI/ML powered insights.
We’re seeking an individual with prior experience in delivering data or analytics engineering outputs, who is keen to work through challenging problems, and possesses a strong aptitude for Python and software engineering principles. You will help us grow our capabilities in supporting the machine learning lifecycle.
Our team is responsible for ensuring our analytics and data infrastructure aligns with best practices in engineering and operations. You will contribute to and learn from a culture focused on technical excellence, reusability, continuous improvement, and the operationalisation of data science.
What you’ll do:
- Undertake technical discovery with stakeholders, and understand the business context in designing and implementing solutions for data pipelines, models and applications.
- Hands-on development of robust and scalable data pipelines and models using Python, SQL, and our evolving data stack (e.g., DBT, Snowflake, and MLOps tooling).
- Model data for optimal workload consumption, eg analytical, BI, ML modelling.
- Contribute to our library of analytical assets, including our feature store, enabling self-service analytics and reducing time spent on data wrangling.
- Collaborate with Data Scientists and Engineers to build and maintain end-to-end machine learning pipelines for training, inference, and monitoring at scale.
- Develop and maintain infrastructure, tooling, and monitoring for data applications and reproducible data science workflows.
- Test and deploy data assets and ML pipeline components to production environments, adhering to software engineering best practices (e.g., version control, CI/CD, testing).
- Identify and expose technical debt and related issues for resolution, contributing to the overall health and maintainability of our data ecosystem.
- Stay current with emerging practices, techniques, and frameworks in applied machine learning, data engineering, and analytics engineering.
What you’ll bring:
Minimum of 2 years experience in data engineering, analytics engineering, ML engineering, or a software engineering role with a data focus.
Strong proficiency in Python for data processing, pipeline development, and scripting. Experience with ML libraries (e.g., scikit-learn, pandas, NumPy) is highly desirable.
Proven experience in developing, deploying, and maintaining data pipelines and data models in production environments.
Familiarity with software engineering best practices (e.g., Git, CI/CD, testing, code reviews).
Solid experience with SQL for data querying, transformation, and optimization.
Experience or a strong interest in machine learning concepts and MLOps (e.g., model deployment, monitoring, versioning) is a significant plus.
Experience with cloud data warehouses like Snowflake is highly desirable.
Experience with data pipeline orchestration tools like Airflow, Dagster, Prefect and data modeling tools like DBT is desirable.
We know this is a comprehensive list, and you may not meet every single requirement. If you believe you have the core skills and the passion to grow into this evolving role, we still want to hear from you!
This is a great opportunity to shape the future of analytics and product science at Xero.
Why Xero?
Offering very generous paid leave to use however you’d like (plus statutory holidays!), dedicated paid leave to care for your physical and mental wellbeing as well as an Employee Assistance Program to access mental health care for you and your family. Health insurance, life insurance, and income protection.
We offer wellbeing and sports programmes, employee resource groups, 26 weeks of paid parental leave for primary caregivers, an Employee Share Plan, beautiful offices, flexible working, career development, and many other benefits that reflect our human value.
You’ll do the best work of your life at Xero!
