Lead Data Engineer - Auctions & Outcomes

Kargo•New York, NY

9d•$200,000 - $230,000

About The Position

Kargo is building toward a unified platform where advertisers run campaigns across CTV, web, mobile apps, and social entirely self-serve – the first time that capability goes directly into clients' hands. This role raises the bar on the pipelines behind it, which already process billions of events per hour and now need stability, speed, a consistent vocabulary across surfaces, and observability that catches problems before clients do (for engineers and AI agents alike). In this role, you'll own that work across supply and demand, measurement, reporting, and the log processing pipelines underneath. You'll also set the technical bar for the domain's engineers.

Requirements

Owned large-scale, interdependent data systems in production, with deep Spark expertise: idiomatic, testable transformations tuned for cost and performance, built on Python, Airflow and Iceberg.
Led engineers, setting direction, reviewing work, developing people, while staying hands-on.
Hands-on with infrastructure: comfortable in AWS and Kubernetes, able to dig into logs and metrics to work out why a workload is failing or running slowly, and glad to pass that on.
Engage analysts and business stakeholders directly, on metric definitions, not just requirements, and turn ambiguity into a sequenced roadmap, managing dependencies across teams and saying what isn't getting done.
Fluent with AI tooling in your own workflow, and reason about what makes a codebase and its data legible to it.

Nice To Haves

Exchange, SSP or DSP experience: log-level auction and bidstream data at scale.
Measurement and attribution in AdTech: pixels, trackers, 3P measurement partners.
Migrating data systems: SQL to Spark, batch to streaming with Kafka or Redpanda.
Snowflake, where reporting aggregation still lives, plus Clickhouse or similar.
CI/CD with GitHub Actions/ArgoCD; monitoring with VictoriaMetrics/Prometheus/Grafana.
Building internal tooling or libraries that other engineering teams adopted.
Consolidating or migrating data systems following an acquisition.

Responsibilities

Own business-metric reporting for data engineering: publisher performance, campaign delivery, revenue and spend, and attribution. Shape the definitions that make a cross-surface campaign read the same everywhere.
Own the domain's data sharing: the reports publishers and advertisers receive, log-level data for partners and clients, and our data sharing API.
Level up the event log processing pipelines at the heart of the domain: idiomatic Spark, refactored for testability, and a path to streaming as demand for faster insights grows.
Own and raise the bar on the domain's observability and alert response. Inventory today's signals, monitors and alerts, centralize them, and bring each to standard: a freshness and quality commitment, context for AI-assisted triage, and a runbook.
Lead and grow the domain's data engineers. Define the standards for testability, cost efficiency and the patterns worth repeating, then raise the team to them through your own code, reviews, and knowledge-sharing, recording decisions in ADRs.