Senior Data Engineer

The Opportunity

Kargo is hiring a Senior Data Engineer to build and scale the data infrastructure that powers our targeting, identity, and measurement capabilities. This role owns hands-on implementation of the pipelines, systems, and tooling that turn raw data into competitive advantage—at speed and at scale. When this role is firing, data flows reliably, engineering teams move faster, and Kargo's data products are built on a foundation that can handle whatever comes next. This is a remote role based in London, UK.

The Daily To-Do

• Build, optimize, and operate core ETL/ELT pipelines—assessing bottlenecks, documenting SLAs, and catching incidents proactively rather than reactively
• Integrate new data sources end-to-end, activating targeting and measurement use cases that perform against defined KPIs
• Stand up and maintain ArgoCD-based CI/CD deployment pipelines that accelerate team velocity and reduce rollback events
• Implement privacy-compliant identity and targeting methodologies that meet GDPR/CCPA standards—built into the stack, not bolted on after the fact
• Own infrastructure deployment and scaling decisions across AWS, EKS, Docker, and Kubernetes, taking operational responsibility for the systems you build
• Write technical documentation, runbooks, and best practices clear enough that a new engineer can onboard to a system without a walkthrough
• Contribute actively in design and brainstorming sessions—bringing strong opinions, updating them when better ideas emerge, and communicating decisions clearly to technical and non-technical stakeholders alike

Qualifications

• Deep proficiency in Python and Spark for building and optimizing large-scale data pipelines, with a strong grasp of data structuring for performance and efficiency
• Hands-on experience with Airflow for orchestrating complex data workflows, and Apache Iceberg for table format management at scale
• Extensive AWS experience including EKS, Docker, and Kubernetes; comfortable owning infrastructure deployment and scaling decisions
• CI/CD fluency—ArgoCD, automated testing pipelines, and monitoring via Prometheus—treating deployment reliability as a first-class concern
• Familiarity with analytical warehousing in Snowflake or ClickHouse, including SQL optimization, cost management, and performance tuning
• Working knowledge of identity, privacy, and targeting methodologies in ad tech—understanding how data flows through the ecosystem and where privacy constraints apply
• Preferred: experience with real-time streaming technologies (Kafka, Flink) and familiarity with Aerospike or similar low-latency key-value stores used in high-throughput targeting systems