As an Analytics Engineer, you'll build data pipelines, models, and dashboards, collaborate with teams to ensure data quality, and perform analyses to support decisions.
Position Summary
As an Analytics Engineer, you'll be the backbone of our data infrastructure — building the pipelines, models, and dashboards that power decisions across the business. You'll work closely with Data Scientists, Product, and GTM teams to transform raw data into reliable, scalable assets that drive real outcomes. This is a high-impact role for someone who takes pride in clean, well-documented code, thrives in a fast-moving environment, and wants to see their work directly influence how a growing company operates and scales
How you'll create impact:
Build and maintain feature marts for machine learning pipelines, working closely with our Data Scientist to ensure production-ready data models
Design and implement data transformations using DBT, creating reliable, well-documented data pipelines from multiple source systems
Create and manage datasets that enable self-service analytics across Product, GTM, and Customer Success teams
Develop dashboards and reports in QuickSight for key stakeholders, translating business questions into actionable insights
Perform ad-hoc analyses to support product and business decisions, moving quickly to answer critical questions
Ensure data quality and reliability through testing, documentation, and monitoring
Collaborate across teams to understand data needs and deliver solutions that scale
What we need from you:
Essential
Experience: 3-5 years of experience in analytics engineering, data engineering, or similar roles
Autonomy: Ability to work autonomously in a fast-paced, evolving environment
Strong communication skills: you can translate technical concepts for non-technical stakeholders
Strong SQL skills - you're comfortable writing complex queries, optimising performance, and working with large datasets
Experience with DBT (or similar transformation tools) - you understand data modeling best practices and can build maintainable transformation pipelines
Data visualisation experience - QuickSight preferred, but experience with other BI tools (Tableau, Looker, Power BI) is valuable
Python for data manipulation - pandas, basic scripting, data wrangling
AWS data services - hands-on experience with Redshift, Athena, S3, or similar cloud data platforms
Nice to have
- Experience in e-commerce or SaaS environments
- Familiarity with ML feature engineering and productionization
- Experience with data orchestration tools (Airflow, Dagster, etc.)
- Understanding of data governance and documentation practices
- Experience working in high-growth or startup environments
1 Month Success
Get up to speed on our existing data infrastructure, understand the key source systems, and make your first meaningful contribution — whether that's improving documentation, fixing a pain point in an existing pipeline, or shipping a small but valuable dashboard. You'll be asking the right questions and building trust with stakeholders across Product, GTM, and Data Science.
6 Months Success
You will have taken clear ownership of the data layer — feature marts are reliable and well-documented, self-service analytics are being actively used by non-technical teams, and Machine Learning models aren’t blocked waiting on data. You'll have established good practices around testing and data quality, and stakeholders are coming to you proactively with questions rather than the other way around.
1 Year Success
Be a trusted data partner across the business. The infrastructure you've built is scalable and low-maintenance, ML pipelines have clean production-ready feature sets, and the company is making faster, more confident decisions because of the foundation you've laid. You'll have identified and driven at least one initiative that meaningfully changed how the business uses data — not just responding to requests, but helping shape the roadmap.
Workplace benefits:
Work remotely in the AU
12 weeks of Paid Family Leave at 100%
Exposure to the most influential eCommerce brands globally
Office stipend setup
Opportunities for training + development
Data backed and competitive compensation strategy
4 weeks of annual leave
11 paid public holidays
Sick & carer’s leave
Compassionate & bereavement leave
What we value:
One team
We are one team committed to the same mission. We trust, respect, and value each other. We recognise the unique skills, experiences, and perspectives each of us has to offer. We continually look for ways to support and enable our teammates.
Champion the customer
Our customers are the heart of our business and the pursuit of their success is our north star, At every step, we prioritise their interests in our thinking and actions.
Strive for excellence
We commit to excellence as our standard. We set and achieve ambitious goals. We maintain a bias for action, tackle the hard problems, and continually work to improve.
Extreme ownership
We own the outcomes. We take the necessary action to get things done. We don’t blame others or find excuses. We proactively look for solutions and solve problems.
Integrity always
We are always honest, trustworthy, and professional. We treat others fairly and with respect. We are transparent and forthright. We take our commitments seriously and deliver what we promise.
Always day one
It’s always Day 1 at Okendo. If we’re not growing, we’re dying. We prioritise agility over bureaucracy. Velocity over perfection. Outcomes over process. We move fast, learn, iterate, and adapt.
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Top Skills
Aws Athena
Aws Redshift
Aws S3
Dbt
Python
Quicksight
SQL
Okendo Sydney, New South Wales, AUS Office
333 George St, Level 5, , , Sydney, New South Wales, Australia, 2000
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