Ai-native Sr Data Platform Engineer - Customer Journey Analytics

Adobe Adobe · Enterprise · Bucharest, Romania

The AI-Native Sr Data Platform Engineer role at Adobe focuses on advancing the data and service foundations for Adobe Analytics and Customer Journey Analytics. This role is not purely AI or traditional big-data, but emphasizes the quality of platforms supporting AI systems. Responsibilities include building and operating distributed systems, event streaming, data pipelines, and scalable REST services, with a strong focus on integrating AI/ML capabilities, critical thinking about AI observability and failure modes, and adhering to responsible AI principles.

What you'd actually do

  1. Advance the data and service foundations behind Adobe Analytics and Customer Journey Analytics.
  2. Be comfortable integrating AI/ML capabilities into production systems.
  3. Think critically about observability, evaluation, and failure modes for AI-assisted features.
  4. Care about responsible AI principles (privacy, transparency, safety) when shipping real user‑facing functionality.

Skills

Required

  • 10+ years of hands‑on experience building and operating distributed systems, backend services, or large‑scale data platforms.
  • Strong experience with event streaming and messaging systems (Kafka, Kafka Streams, Flink, Kinesis, or similar).
  • Experience building data pipelines using Spark, Databricks, Flink, or comparable technologies.
  • Solid understanding of operating production systems: Kubernetes, CI/CD, monitoring, alerting, and incident response.
  • Experience crafting and operating scalable REST services.
  • Strong engineering fundamentals (Java preferred; Python familiarity a plus).

Nice to have

  • Scala experience.
  • Experience with Jenkins, Argo, or similar CI/CD tooling.
  • Strong observability background (Prometheus, Grafana, Splunk, Fluentbit).
  • Experience operating systems during high‑traffic or event‑driven spikes.
  • Prior exposure to AI‑assisted features, LLM APIs, or AI‑powered services in production.
  • Familiarity with monitoring AI/ML systems (telemetry, drift, performance signals).

What the JD emphasized

  • AI here is embedded within the system, not a nice-to-have.

Other signals

  • integrating AI/ML capabilities into production systems
  • observability, evaluation, and failure modes for AI-assisted features
  • responsible AI principles (privacy, transparency, safety)