Sr. Data Engineer

Responsibilities:

• Design and own real-time and batch data pipelines using Kafka, Spark, and Flink, processing large-scale in-game event data with low latency at production scale (millions+ events/day).
• Architect fault-tolerant, horizontally scalable systems on Google Cloud (GCP), making key design decisions on infrastructure, not just implementing specs.
• Optimize BigQuery for both query performance and cost, including partitioning, clustering, and query optimization strategies.
• Lead technical collaboration with analytics, data science, and engineering teams, translating business/product requirements into robust data architecture.
• Mentor junior engineers and contribute to best practices, code reviews, and pipeline design standards.
• Troubleshoot and resolve production incidents in streaming pipelines (data loss, lag, schema drift).

Requirements:

• 5+ years of hands-on experience in data engineering, with strong production-grade Python.
• 3+ years of production experience with Kafka and Spark or Flink in high-throughput, low-latency environments.
• Deep understanding of distributed systems and streaming data concepts (exactly-once semantics, windowing, backpressure, and partitioning strategies).
• Proven experience owning pipeline architecture end-to-end, not just building to spec, but making design trade-off decisions.
• Hands-on experience with GCP (BigQuery, Dataflow, Pub/Sub, GCS); AWS/Azure equivalent experience is transferable if strong.
• Experience optimizing data warehouse costs and performance at scale.
• Bonus: experience in gaming, ad-tech, or other high-volume event-streaming industries (real-time analytics on user behavior).
• Strong communication skills: this role bridges engineering, DS, and analytics.