Senior Data Engineer, Kargo

• Deep proficiency in Python and Spark for building and optimizing large-scale data pipelines; 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; treats deployment reliability as a first-class concern
• Familiarity with analytical warehousing in Snowflake or ClickHouse — SQL optimization, cost management, and performance tuning
• Working knowledge of identity, privacy, and targeting methodologies in ad tech — understands how data flows through the ecosystem and where privacy constraints apply
• Drives implementation independently from ambiguous brief to working system — doesn't wait for perfect specs to start building
• Takes operational responsibility for the systems they build; monitors, iterates, and improves without being prompted
• Brings structured thinking to complex, multi-system challenges; breaks down problems clearly before reaching for a solution
• Optimizes for the right outcome — performance, cost, reliability — not just the fastest path to "done"
• Contributes actively in design and brainstorming sessions; brings strong opinions and is equally willing to update them when better ideas emerge
• Communicates technical decisions clearly to both engineering peers and non-technical stakeholders without oversimplifying
• Writes documentation that actually gets used — clear, thorough, and maintained as systems evolve
• Treats onboarding others as part of the job; leaves systems better understood than they found them

Desirable

• The Senior Data Engineer exists to build and scale the data infrastructure that powers Kargo's 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
• Outcomes — What Success Looks Like in 6–12 Months
• Core ETL/ELT pipelines are optimized and operationally reliable — Existing pipelines are assessed, bottlenecks are addressed, and data flows with documented SLAs; incidents are caught proactively, not reactively
• New datasets are onboarded and powering targeting capabilities — At least one meaningful new data source is integrated end-to-end, with targeting or measurement use cases activated and performing against defined KPIs
• CI/CD practices are adopted and accelerating team velocity — ArgoCD-based deployment pipelines are in place and the team is shipping with greater speed and fewer rollback events as a result
• Privacy-compliant data methodologies are implemented and documented — Modern approaches to identity and targeting that meet GDPR/CCPA standards are built into the stack, not bolted on after the fact
• Technical documentation is thorough and actually used — Implementation docs, runbooks, and best practices are written clearly enough that a new engineer can onboard to a system without a walkthrough