Data Platform Engineer

Bangalore, Karnataka, India

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

Who We Think Will Be A Great Fit

* Architectural Depth: Demonstrates a deep understanding of how platform components interact and scale within a global data ecosystem. * Innovation & Efficiency: Drives the adoption of new technologies and methodologies to improve platform performance and reduce operational overhead. * Problem Solving: Resolves complex, high-impact technical issues related to distributed systems and data availability. * Collaboration: Partners effectively with Data Scientists, Data Engineers, and Studio Leads to provide a high-quality developer experience. * Strategic Thinking: Anticipates future platform needs and aligns architectural decisions with long-term business goals.

* Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field. * Experience: 6+ years of professional experience in data platform or distributed systems engineering in high-volume environments. * Core Technical Stack: Mastery of SQL and Python; expert knowledge of big data processing frameworks like Spark. * Cloud Ecosystem: Deep proficiency in AWS services (S3, EKS, IAM, SNS/SQS) and extensive experience with Snowflake or Databricks. * Orchestration: Experience building or extending workflow orchestration platforms like Apache Airflow. * Architecture Mindset: Strong understanding of data modeling methodologies (Kimball, Inmon, Data Vault) and how they apply to modern cloud data platforms.