What You Will Own

• Own the definition, generation, quality, and consumption of data across the Identity and Financial Crime domains — what gets produced, what it means, what guarantees come with it, and who is accountable when those guarantees are not met.
• Make data quality an engineering discipline rather than a downstream cleanup exercise: producer/consumer contracts, freshness and integrity monitoring, lineage, a named owner for every critical dataset, and a real path from a detected anomaly to a fix at source. You'll be measured on whether downstream teams stop discovering data problems by accident.
• Own the models and analytics behind how Zepz detects fraud and financial crime — feature engineering, model development, deployment, monitoring, and retraining. You own model performance in production, not just model development.
• Lead a mixed organization of Data Scientists, Machine Learning Engineers, and Analytics Engineers. These are three different crafts with three different career paths, and leading them well means understanding all three rather than favoring the one you came from.
• Partner as a peer with the Engineering Manager who owns the fraud detection platform and reports directly to the VP Engineering. Your models run on their platform: you own model outcomes, they own platform outcomes, and you jointly own the seam between them.
• Partner with our central data function, which owns core data infrastructure and platform tooling. You are a demanding customer and a collaborator, not a replacement — you build domain data products and quality practice on top of what they provide, and feed real requirements back to them.
• Set standards with Identity engineering teams who do not report to you. Much of your impact depends on changing how data is produced upstream, which is influence work: making the cost of poor data visible with evidence rather than assertion, and making it easy for those teams to do the right thing.
• Create high-performing teams with a bias toward shipping without sacrificing reliability or trust in the numbers. Use technical and data debt as a tool to move faster, while ensuring your teams have the space to build it right once they know what the right thing to build is.
• Hire, retain, and level up superlative talent to ensure our team remains world-class as we continue to scale and launch into new markets.
• Roll up your sleeves when models degrade or data breaks. You'll need to discuss technical solutions, review work in depth, and help execute strategy with your leads.

In Your First Few Months, You Will:

• Establish an honest, evidenced baseline of data quality across the identity-to-financial-crime path — what breaks, how often, who finds out, and what it costs in downstream hours and model degradation. Bring the number, not the anecdote.
• Agree a written ownership model with the central data team and the Identity engineering leads: who owns which datasets, what the contracts are, and what happens when one is broken.
• Instrument the critical path, so data issues are detected by monitoring rather than by an analyst noticing that something looks odd.
• Get to know the models — how they perform, how they degrade, how retraining actually works today, and where risk is concentrated.
• Assess the team: identify the gaps, the flight risks, and the people ready for more, and start hiring against a plan rather than against a vacancy.
• Promote a culture of ownership, accountability, and shipping.

What You Bring to the Table

• You have a track record of leading a data organization of comparable size (roughly 15–25 people) and of supporting the professional development of managers and senior-level ICs.
• Track record of leading mixed technical teams — Data Scientists, ML Engineers, Analytics Engineers — with genuine credibility across all three crafts.
• Demonstrable ownership of data quality at scale: contracts, observability, lineage, semantic consistency — with evidence of the downstream impact, not just the tooling you installed.
• Fluency with modern lakehouse and analytics engineering practice: Databricks (or equivalent), dbt-style transformation, feature store concepts and real-time feature serving, and the discipline of treating datasets as products with owners and SLAs.
• Experience with production ML in a real-time context: model monitoring, drift detection, and retraining pipelines.
• Proven experience with AWS at scale.
• Direct experience leading FinCrime/Fraud or Identity engineering or data science teams (not just adjacent risk/security).
• The ability to get outcomes from teams that don't report to you. We'd rather hear about a time you changed a producer team's behavior than a time you escalated.
• At least 8+ years of technical experience in a hybrid IC/management role.
• You can attract and retain top talent and lead teams with diverse skill sets.
• You work well with Engineering and Product leaders and are comfortable building effective relationships with folks outside of the Prod-Eng spheres.
• You have exceptional judgment; we're scaling quickly — you'll be required to make good tradeoffs.

FinCrime & Identity Domain

• Bring prior experience leading FinCrime/Fraud or Identity teams — understanding of typologies (card fraud, ATO, first-party fraud, synthetic identity, money mule networks), the regulatory context, and how detection strategy trades off against customer experience.
• Understand identity verification, KYC, and onboarding data as a data domain, not just as a product surface.
• Partner with Risk, Compliance, and Legal on regulatory obligations tied to fraud and identity controls.
• Own incident response leadership when major fraud events, model failures, or data integrity failures occur.

Bonus Points

• Have worked at a scaling startup previously.
• Have experience with data mesh or domain-oriented data ownership models in practice, not just in theory.
• Have experience working with stablecoin and cryptocurrencies.
• Have experience working with AI in a production environment.
• Have worked in environments with k8s / gRPC / Spring Boot / Java / Python.

AI Literacy & Tools

As Zepz, we're building AI into how we work. We're on a journey to making AI utilization streamline what we do. "For this position, we are looking for:

• Proficient depth of knowledge around automation, AI, and how those can be applied in your area of work — from accelerating analysis and insight generation, to improving how we design and deliver processes and communications.
• Practical experience using AI tools in a professional context