Data Engineer (Smart TV OS)

About the role

You will lead the end-to-end design, construction, and iteration of real-time business data ingestion systems and streaming data warehouse architecture. You will work at the intersection of high-scale real-time data streaming, cloud analytics platforms, and business-critical analytics engines to support real-time dashboards, user profiling platforms, and business monitoring systems.

Key responsibilities

• Own the lifecycle development of real-time collection, cleaning, and warehousing of business logs, user behavior data, and database operational logs to guarantee pipeline stability, completeness, and low latency.
• Design multi-layer real-time data models, build and tune streaming ETL pipelines, and support continuous delivery of real-time business metrics and analytics.
• Maintain and optimize core Kafka streaming infrastructure. Diagnose and resolve online challenges including message backlogs, data skew, message loss, duplication, and partition tuning.
• Develop, tune, and operate real-time Spark/Flink tasks to maximize computing resource utilization, throughput, and sub-second latency.
• Partner with data product managers, analysts, and business stakeholders to translate complex business requirements into scalable, production-ready data pipelines.
• Establish data standardization practices, automated data quality monitoring, alert thresholds, and SLA governance protocols.

About you

• Bachelor's degree or higher in Computer Science, Software Engineering, Data Science, or a related technical discipline.
• 3+ years of professional big data engineering experience in high-scale tech/internet environments with proven track records in real-time pipeline construction.
• Proficiency in Python, Scala, or Java with solid Shell/Linux scripting capabilities.
• Expert-level knowledge of Apache Kafka (partitioning strategies, consumer mechanisms, lag mitigation, deduplication).
• Strong hands-on experience with Apache Spark (Spark SQL) and Apache Flink (Flink SQL) for streaming task optimization.
• Hands-on experience with Change Data Capture (CDC) and Binlog real-time synchronization from operational databases.
• Exposure to cloud-native platforms (Google Cloud Platform / BigQuery, Azure, Databricks, dbt).
• Professional working fluency in English (verbal and written) for cross-border collaboration.