Summary
The Senior Data Engineer will report to the Senior Manager, Data and Analytics. This role is responsible for building and maintaining the data infrastructure that powers analytics, AI/ML workloads, and business insights for the FC Mobile franchise.
Responsibilities:
Design and build scalable data pipeline architecture supporting extraction, transformation, and loading of data from game telemetry, live services, and third-party sources using SQL and AWS technologies.
Assemble large, complex datasets that meet functional and non-functional requirements for analytics, machine learning, and AI-powered applications.
Identify and implement internal process improvements: automating manual processes, optimizing data delivery, and re-designing infrastructure for greater scalability and reliability.
Build and maintain analytics tools and AI-enabled data products that provide actionable insights into player behavior, customer acquisition, operational efficiency, and other key performance metrics.
Collaborate with game team leadership, Product, Analytics, and Design teams to support their data infrastructure needs and resolve data-related technical issues.
Ensure data security and compliance across international boundaries through multiple data centers and AWS regions.
Develop and maintain data tools, including AI/ML feature pipelines, that empower analytics and data science team members to accelerate experimentation and product development.
Qualifications:
5-8 years of experience in a Data Engineer role in the technology or gaming industry.
Advanced working experience with relational SQL and NoSQL databases, including PostgreSQL and Hive.
Advanced experience with big data tools: Hadoop, Presto, Spark, Kafka, and experience building large-scale data pipelines, architectures, and datasets.
Experience with data pipeline and workflow management tools such as Airflow, Azkaban, or Luigi.
Experience with AWS cloud services: EC2, S3, Aurora, Redshift, and related managed services (Glue, Athena, SageMaker).
Familiarity with AI/ML engineering practices: building feature stores, supporting model training pipelines, deploying inference endpoints, or integrating large language model (LLM) APIs into data workflows.
Experience with Python data ecosystem (pandas, NumPy, requests, matplotlib) and comfort working alongside machine learning libraries (scikit-learn, PyTorch, or similar).
Working knowledge of message queuing, stream processing, and scalable data stores.
Strong project management and cross-functional communication skills.
Game industry experience is a plus.