Position SummaryAs 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 scalesHow you'll create impact:Build and maintain feature marts for machine learning pipelines, working closely with our Data Scientist to ensure production-ready data modelsDesign and implement data transformations using DBT, creating reliable, well-documented data pipelines from multiple source systemsCreate and manage datasets that enable self-service analytics across Product, GTM, and Customer Success teamsDevelop dashboards and reports in QuickSight for key stakeholders, translating business questions into actionable insightsPerform ad-hoc analyses to support product and business decisions, moving quickly to answer critical questionsEnsure data quality and reliability through testing, documentation, and monitoringCollaborate across teams to understand data needs and deliver solutions that scaleWhat we need from you:EssentialExperience: 3-5 years of experience in analytics engineering, data engineering, or similar rolesAutonomy: Ability to work autonomously in a fast-paced, evolving environmentStrong communication skills: you can translate technical concepts for non-technical stakeholdersStrong SQL skills - you're comfortable writing complex queries, optimising performance, and working with large datasetsExperience with DBT (or similar transformation tools) - you understand data modeling best practices and can build maintainable transformation pipelinesData visualisation experience - QuickSight preferred, but experience with other BI tools (Tableau, Looker, Power BI) is valuablePython for data manipulation - pandas, basic scripting, data wranglingAWS data services - hands-on experience with Redshift, Athena, S3, or similar cloud data platformsNice to haveExperience 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 SuccessGet 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 SuccessYou 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 SuccessBe 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 AU12 weeks of Paid Family Leave at 100%Exposure to the most influential eCommerce brands globallyOffice stipend setupOpportunities for training + developmentData backed and competitive compensation strategy4 weeks of annual leave11 paid public holidaysSick carer’s leaveCompassionate bereavement leaveWhat we value:One teamWe 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 customerOur 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 excellenceWe 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 ownershipWe 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 alwaysWe 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 oneIt’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. Follow Us:InstagramLinkedinOkendo" rel="nofollow ugc noopener noreferrer" target="_blank">TwitterFacebook.