Senior Data Engineer

Nebius is positioning itself at the forefront of the global AI infrastructure revolution, offering a comprehensive, full-stack cloud platform designed to simplify the complexities of AI and machine learning workflows. As they continue to scale, they are looking for a Senior Data Engineer to take full ownership of the data platforms that drive their analytical capabilities and operational success. This role is inherently remote, offering a unique opportunity for South African professionals to contribute to high-impact, global-scale AI infrastructure from a local base.

About the role

This is a position for a seasoned engineer who thrives on turning abstract business requirements into robust, scalable technical realities. You will be responsible for the entire lifecycle of data products—from initial architectural design and pipeline development to long-term operational maintenance.

As a senior member of the team, you will act as a bridge between technical infrastructure and business strategy. Your input will be vital in ensuring that as Nebius grows, its data ecosystem remains reliable, performant, and easy for other internal teams to navigate. You will not just be writing code; you will be establishing the tools, conventions, and automation that define how the entire organisation interacts with data.

The core of this role revolves around the reliability and scalability of data platforms. You will be expected to:

• Design and implement high-performance data pipelines and complex data models that serve as the backbone for analytics and business intelligence.
• Evolve the company’s data architecture, selecting appropriate storage and processing patterns to handle significant data workloads.
• Enhance data observability, ensuring that lineage is clear and that data quality is maintained across the ecosystem.
• Troubleshoot and resolve critical production issues, focusing on performance bottlenecks and data correctness.
• Define data contracts and priorities in collaboration with product and business stakeholders.
• Mentor junior colleagues through code reviews, pair programming, and architectural design sessions.
• Participate in on-call rotations, maintaining the operational health of the systems you build.

What they're looking for

To be successful in this role, you should have at least five years of experience in data or backend engineering, with a strong track record of deploying systems to production. Technical proficiency is paramount, but the ability to articulate trade-offs in an ambiguous environment is what distinguishes a senior candidate.

• **Technical Stack:** You must have advanced proficiency in Python and SQL, with the ability to write production-ready code and optimise complex queries.
• **Orchestration:** Hands-on experience with industry-standard workflow orchestration tools, such as Airflow, Dagster, or Prefect, is essential.
• **System Design:** A deep understanding of data modelling, storage trade-offs, and architecture is required. You should be comfortable designing systems that are testable, monitorable, and resilient.
• **Collaboration:** You should be capable of working across functional lines, explaining complex technical decisions to non-technical stakeholders, and influencing the team’s long-term technical direction.

For a South African candidate, this role represents an entry into the high-growth sector of global AI infrastructure. Data Engineering remains one of the most lucrative fields in the local ICT sector. Roles of this seniority typically demand a high degree of autonomy and are often remunerated at a premium, especially when working for international, remote-first organisations. Growth from this position usually leads into Lead Data Architect or Engineering Manager pathways, as the skills gained here—specifically in observability and large-scale orchestration—are highly sought after by local enterprises undergoing digital transformation and cloud migration.

• 5+ years of experience in data engineering, backend engineering, or a related role; or equivalent experience delivering and operating production data systems.
• Proven experience independently delivering complex data pipelines or data-platform capabilities from problem definition through production operation.
• Strong Python and SQL skills, including writing maintainable production code and optimizing non-trivial queries.
• Hands-on experience with workflow orchestration tools such as Airflow, Prefect, or Dagster.
• Strong understanding of data modeling, including designing maintainable analytical models and data contracts for multiple consumers.
• Solid knowledge of data architectures and storage systems, including the trade-offs between different processing and storage approaches.
• Experience designing for reliability: testing, monitoring, data-quality validation, alerting, debugging, and incident resolution.
• Ability to make technical decisions under ambiguity, explain trade-offs clearly, and collaborate effectively with engineers and non-technical stakeholders.