Build, maintain, and optimize real-time data pipelines to process bid requests, impressions, clicks, and user engagement data.
Develop scalable solutions using tools like Apache Flink, Spark Structured Streaming, or similar stream processing frameworks.
Collaborate with backend engineers to integrate OpenRTB signals into our data pipelines and ensure smooth data flow across systems.
Ensure data pipelines handle high-throughput, low-latency, and fault-tolerant processing in real-time.
Write clean, well-documented code in Java, Scala, or Python for distributed systems.
Work with cloud-native messaging and event platforms such as GCP Pub/Sub, AWS Kinesis, Apache Pulsar, or Kafka to ensure reliable message delivery.
Assist in the management and evolution of event schemas (Protobuf, Avro), including data consistency and versioning.
Implement monitoring, logging, and alerting for streaming workloads to ensure data integrity and system health.
Continuously improve data infrastructure for better performance, cost-efficiency, and scalability.

3-5+ years of experience in data engineering, with a strong focus on real-time streaming systems.
Familiarity with stream processing tools like Apache Flink, Spark Structured Streaming, Beam, or similar frameworks.
Solid programming experience in Java, Scala, or Python, especially in distributed or event-driven systems.
Experience working with event streaming and messaging platforms like GCP Pub/Sub, AWS Kinesis, Apache Pulsar, or Kafka.
Hands-on knowledge of event schema management, including tools like Avro or Protobuf.
Understanding of real-time data pipelines, with experience handling large volumes of event-driven data.
Comfortable working in Kubernetes for deploying and managing data processing workloads in cloud environments (AWS, GCP, etc.).
Exposure to CI/CD workflows and infrastructure-as-code tools such as Terraform, Docker, and Helm.

Data Engineer – Streaming Platform

Key skills