Principal Data Engineer

Our technology stack is designed to run on public cloud architectures, notably AWS and GCP

Development is organised around Kotlin and Python for our backend languages and TypeScript/ES6+React for our frontend stack

We make substantial use of relational database technologies, notably Postgres, Yugabyte

We also use an event-sourced model powered by Kafka for our communication bus and gRPC for our intra-service communication protocol

We use modern observability solutions from Grafana Cloud and deploy our code using ArgoCD

We have a strong emphasis on engineering excellence and strive to ship the best possible code and the best possible solutions to our customers

As a Principal Data Engineer with deep impact in a real-time environment, you will bring:

Substantial experience designing and operating production-grade data platforms at high scale, whether that is large request volumes, large data volumes, or both.

Deep expertise in distributed data systems: streaming (Kafka or similar), batch and ELT/ETL frameworks (Spark, Flink, dbt, Airflow or Argo Workflows), and modern lakehouse or warehouse technologies.

Strong production Python experience and awareness of other relevant languages (Java/Kotlin) sufficient to set direction, review code and coach others.

Experience designing for cloud (AWS and GCP) and containerised infrastructure (Kubernetes, Docker, ArgoCD).

A track record of treating data quality, observability and data contracts as first-class engineering concerns rather than afterthoughts.

Strong working understanding of logging, monitoring, alerting and incident management tooling for data systems.

Excellent written and verbal communication. You can produce technical documentation that senior leaders and engineers can act on.

Ownership of software and data products from inception through to production and long-term operation.

A track record of coaching staff, senior and mid-level engineers, and of helping Recruiting improve hiring and onboarding.

Nice to have

Experience building or operating data systems in financial services, AML, KYC, fraud, regtech or another regulated domain.

Familiarity with knowledge graph and entity resolution problems: deduplication, linkage, hierarchies and temporal relationships.

Experience supporting ML, LLM and agentic AI workloads, including feature stores, vector stores, retrieval pipelines, tool data and online/offline parity.

Experience representing engineering externally at conferences, meet-ups or in technical publications.

Our mission is to empower every business to eliminate financial crime.

By harnessing AI, a unified platform, and an extensive partner ecosystem, we help customers turn compliance into a catalyst for growth, operational resilience, and enduring regulatory trust.

More than 3,000 enterprises across 75 countries rely on our end-to-end platform and the world’s most comprehensive financial crime risk intelligence. With full-stack agentic automation, we help organizations automate up to 95% of KYC, AML, and sanctions reviews, cut onboarding times by 50%, reduce false positives by 70%, and handle 7x more work with the same staff.

ComplyAdvantage is headquartered in London and has global hubs in New York, Lisbon, Singapore, and Cluj-Napoca. It is backed by Balderton Capital, Index Ventures, Ontario Teachers’ Pension Plan, Goldman Sachs, and Andreessen Horowitz. Learn more about compliance re-engineered for the age of AI at complyadvantage.com .