Job Description

We are seeking an experienced Senior Data Engineer to join our fast-growing global team. In this role, you will lead the end-to-end design, construction, and iteration of our 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

Ingestion Pipeline Ownership: 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.
Real-Time Data Warehouse Architecture: Design multi-layer real-time data models, build and tune streaming ETL pipelines, and support continuous delivery of real-time business metrics and analytics.
Kafka & Streaming Optimization: Maintain and optimize core Kafka streaming infrastructure. Diagnose and resolve online challenges including message backlogs, data skew, message loss, duplication, and partition tuning.
Spark & Flink Operations: Develop, tune, and operate real-time Spark/Flink tasks to maximize computing resource utilization, throughput, and sub-second latency.
Cross-Functional Collaboration: Partner with data product managers, analysts, and business stakeholders to translate complex business requirements into scalable, production-ready data pipelines.
Data Quality & Governance: Establish data standardization practices, automated data quality monitoring, alert thresholds, and SLA governance protocols.

Required Experience & Qualifications

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

Preferred Qualifications

Prior experience studying or working internationally or collaborating across cross-border remote tech teams.
Hands-on knowledge of real-time SLA governance, incident post-mortem processes, and automated alerting frameworks.