Data Engineer

Mission

Neko is redefining what prevention means, from treating illness when it arrives, to sustaining health before it's ever at risk. Our mission: make data-driven, preventative care accessible to more people, before symptoms appear.

In a single, non-invasive visit under an hour, proprietary technology and direct clinical care combine to deliver personalised, actionable insights. It's a team that thinks in 10x, not 10%. Every role here plays a part in building a world where prevention is the norm, and where your work genuinely helps people live longer, healthier lives.

Role Purpose

As a Data Engineer within our Data-to-AI platform, you will play a critical role in enabling healthcare innovation through robust, scalable, and secure data systems. You will design and build core data platform components, advanced data models, and ingestion frameworks that power analytics, machine learning, and data-driven decision making across the organization. Working with sensitive healthcare data, you will ensure strong governance, data quality, and regulatory compliance remain foundational to the platform.

What You’ll Deliver in the First 6–12 Months

• Build and productionize scalable batch and streaming data pipelines supporting platform and research workloads.

• Design and implement robust data models across databases and lakehouse systems to support analytics and machine learning.

• Establish strong data quality, monitoring, and lineage practices improving reliability, traceability, and trust in platform data.

• Contribute to scalable and well-governed data platform infrastructure supporting performance, compliance, and growth.

• Partner closely with data scientists, analysts, and engineering teams to enable reliable data contracts and high-quality datasets.

Responsibilities

• Build and own scalable data pipelines for ingestion, integration, and processing across batch and streaming systems.

• Architect and maintain robust data models across databases and lakehouse platforms supporting analytics and ML workloads.

• Develop and own core data platform components and infrastructure.

• Ensure data integrity and quality through monitoring, alerting, lineage, and traceability.

• Manage and optimize data infrastructure including clusters, storage, and compute resources.

• Implement metrics and observability across services using logging, tracing, and monitoring.

• Troubleshoot production issues, pipeline failures, and performance bottlenecks.

• Collaborate cross-functionally with data scientists, analysts, and backend engineers on modelling, governance, and integration.