Description

Key Skills: SQL, Python, Spark, AWS, Snowflake, Databricks, Apache Airflow, Kafka, Data Modeling, Distributed Systems

Good to Have Skills: Experience with high-velocity telemetry from massive live-service ecosystems, Infrastructure as Code (Terraform), data observability and cataloging tools (Monte Carlo, DataHub, Soda), containerization (Docker, Kubernetes), Vector Databases (Pinecone, Weaviate, Milvus), Model Context Protocol (MCP), LLM data tooling and preparation for fine-tuning Large Language Models.

Roles & Responsibilities:

• Lead the design, implementation, and optimization of core data platform components including Snowflake, Databricks, and AWS S3 Data Lakes.
• Manage and scale high-throughput streaming systems using Kafka or Kinesis to support real-time game telemetry and low-latency analytics.
• Develop Data-as-a-Service (DaaS) features that allow Business Units and Studios to onboard data and manage access with minimal friction.
• Optimize Spark and PySpark applications for performance and cost-efficiency across large-scale distributed clusters.
• Design and automate granular access control models to ensure data security and compliance with global regulations like GDPR and CCPA.
• Ensure seamless data flow and interoperability between disparate platforms utilizing technologies like Apache Iceberg to maintain Single Source of Truth.
• Serve as a subject matter expert for the data platform, translating complex business needs into scalable technical requirements and platform features.

Experience Required: 6+ years of professional experience in data platform or distributed systems engineering in high-volume environments. Mastery of SQL and Python with expert knowledge of big data processing frameworks like Spark. Deep proficiency in AWS services and extensive experience with Snowflake or Databricks.

Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field

Key Skills

Education

Any Graduate