Analytics Engineer (Remote) at Okendo

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.