Principal Data Engineer (Fintech)

Location: Hybrid, with employees required to work from the office two days per week. Company hubs are located in London, New York, Lisbon, Singapore, and Cluj-Napoca.

Company

hirify.global provides AI-powered financial crime risk intelligence and compliance products for AML, KYC, sanctions screening, fraud detection, and transaction monitoring.

What you will do

• Set the medium- to long-term technical direction for the data platform and lead architecture across multiple engineering tribes.
• Design and operate petabyte-scale batch, micro-batch, and streaming data systems supporting a real-time financial crime knowledge graph.
• Build ingestion frameworks, event-bus integrations, feature and serving stores, lakehouse infrastructure, orchestration, and developer tooling.
• Establish standards for Kafka-based event sourcing, data quality, schema evolution, contract testing, lineage, freshness, and observability.
• Partner with ML engineers, data scientists, Product, SRE, Customer Risk, Fraud, Knowledge Graph, and Screening teams on platform and AI initiatives.
• Coach engineers, participate in interviews, improve hiring and onboarding, and represent engineering at external events.

Requirements

• Substantial experience designing and operating production-grade data platforms at high scale.
• Deep expertise in distributed data systems, including Kafka or similar streaming technologies and batch or ELT/ETL frameworks such as Spark, Flink, dbt, Airflow, or Argo Workflows.
• Strong production Python experience, with sufficient Java or Kotlin knowledge to set direction, review code, and coach engineers.
• Experience with AWS or GCP and containerized infrastructure, including Kubernetes, Docker, and ArgoCD.
• Experience with data quality, observability, data contracts, logging, monitoring, alerting, and incident management for data systems.
• Strong technical communication skills and a track record of owning software and data products through production and long-term operation.

Nice to have

• Experience in financial services, AML, KYC, fraud, regtech, or another regulated domain.
• Knowledge graph and entity resolution experience, including deduplication, linkage, hierarchies, and temporal relationships.
• Experience supporting ML, LLM, and agentic AI workloads, including feature stores, vector stores, retrieval pipelines, and online/offline parity.
• Experience representing engineering at conferences, meet-ups, or in technical publications.

Culture & Benefits

• Hybrid work model with two required office days per week.
• Equity participation and an unlimited time-off policy.
• Annual learning budget and home office budget.
• Enhanced parental leave, childcare benefits, life insurance, and medical coverage through BUPA.
• Pension contribution through The People's Pension.