Data Engineering Lead

Do you want autonomy to solve problems that matter? To work with a modern data stack (that actually works)? To have direct impact on a global brand's success?

This role has all three. Our data infrastructure (including Kafka, AWS/GCP, Python, R) is solid, our problems are interesting, and your work will directly accelerate how we operate and make decisions. This isn't about maintaining pipelines, it's about turning data into a strategic enabler for the entire business.

So what's the actual work? Diving deep into complex pipeline issues while contributing to our strategic direction and architecture. Bridging engineering excellence and business impact through collaboration across every level of the organization. Strengthening our engineering function alongside a talented team that brings both deep institutional knowledge and fresh perspectives, in a team and company culture that’s (very) low on politics and ego. The goal: building systems that don't just serve the business, they propel it forward.

If you're the kind of engineer who gets genuinely excited about turning chaos into order, who sees tech debt reduction as a strategic opportunity, and who believes the best engineering happens when you're embedded with the business teams who depend on your work, then we should talk. If you prefer pure technical work without collaborating with the stakeholders who will use your data, or pure management without getting your hands dirty in the code, this role isn't for you. We need someone who thrives in both worlds and owns the results.

Join our team that thinks deeply about craft, who choose long-term excellence over quick fixes, and who solve problems that genuinely matter. Join our culture of smart people, with good intentions, who get s**t done. This isn't just another data job; it's where technical depth meets business impact in ways that actually matter.

• At least 4 years of experience in data engineering roles, with demonstrated progression toward technical leadership
• Advanced SQL expertise and hands-on experience building and optimising data pipelines using modern orchestration tools (Apache NiFi, Airflow, or similar)
• Strong debugging skills with the ability to perform root cause analyses on complex data pipeline issues
• Proven ability to maintain and evolve production data infrastructure in cloud environments (AWS/GCP)
• Track record of improving data quality, reliability, and operational excellence at scale
• Experience working directly with business stakeholders to understand their needs and translate them into data solutions
• Demonstrated ownership mindset: you see problems through to resolution rather than passing them along — our systems are stable enough that critical out-of-hours issues come up only a handful of times a year, and we want to keep it that way
• Exceptional precision in your work, knowing how to verify correctness even when dealing with unreliable data

WE’D BE EXCITED IF YOU HAVE (but please apply even if you don’t):

• Hands-on experience with our specific stack: Kafka, Python, R, PostgreSQL, AWS Aurora, Google BigQuery
• Background in physical product companies - you understand e-commerce, inventory and supply chain data challenges
• A track record of successfully mentoring engineers and helping teams level up their technical capabilities
• Project management experience with data initiatives, including prioritization frameworks
• Implementation knowledge of data governance, privacy standards, and security best practices
• Proven ability to build and maintain data science infrastructure (feature stores, ML pipelines)
• A genuine passion for data warehouse organization that borders on the obsessive (in the best way)
• Contributions to data engineering communities, open source projects, or technical writing

• Paired with an engineer to debug a data lag issue, using it as an opportunity to document the solution approach in the team Wiki
• Noticed Amazon data quality alerts recurring, dug into the root causes, and built better monitoring to catch issues earlier—because we'd rather invest time improving our defenses than constantly firefighting
• Triaged the engineering backlog with your team and identified a solution that fixes supplier data quality issues while migrating a NiFi flow to dbt
• Partnered with Finance to define requirements for a new reporting initiative, negotiating for an extra week to build it right rather than rushing a fragile solution that would break later
• Coached an engineer through prioritisation challenges during your one-on-one, helping them communicate a clear plan to stakeholders
• Shaped the data engineering roadmap with other managers, balancing big upcoming projects with critical infrastructure improvements
• Demoed your BigQuery MCP experiments for lineage analysis at the monthly Data Carousel, then learned from the data science team's causal inference work on marketing spend