TP-Link Systems Inc. is seeking a Big Data Engineer to join our Irvine, California team. You will own a component of the data platform end-to-end — its pipelines, tables, quality checks, monitoring, and recovery. Design and build ETL on EMR (Spark) and Airflow, and ingest data from OLTP sources via DataX / CDC, including incremental sync and upstream schema changes. Model the data using dimensional approaches (ODS / DWD / DWS / ADS) to support real analytical use cases. Own data quality for your component, write checks and freshness monitoring, and resolve issues before they reach downstream users. Tune runtime and infrastructure costs for your jobs, and choose the right engine for a workload (StarRocks serving vs. batch on EMR). Build components that other engineers can reuse, and automate repetitive work. Work directly with analysts and business stakeholders to turn requirements into models and datasets that actually get used.

Required qualifications - Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent practical experience. - 2+ years of hands-on data development in a production environment with scheduled pipelines and real downstream consumers. - Experience owning a production pipeline or component, including when it breaks. - Strong SQL: window functions, complex multi-table joins, incremental and idempotent writes; able to read an execution plan and diagnose data skew or slow queries. - Production-grade Python: maintainable, testable ETL and tooling code (PySpark / pandas / boto3) with proper error handling and logging. - Han

TP-Link Systems Inc. is seeking a Big Data Engineer to join our Irvine, California team. You will own a component of the data platform end-to-end — its pipelines, tables, quality checks, monitoring, and recovery. Design and build ETL on EMR (Spark) and Airflow, and ingest data from OLTP sources via DataX / CDC, including incremental sync and upstream schema changes. Model the data using dimensional approaches (ODS / DWD / DWS / ADS) to support real analytical use cases. Own data quality for your component, write checks and freshness monitoring, and resolve issues before they reach downstream users. Tune runtime and infrastructure costs for your jobs, and choose the right engine for a workload (StarRocks serving vs. batch on EMR). Build components that other engineers can reuse, and automate repetitive work. Work directly with analysts and business stakeholders to turn requirements into models and datasets that actually get used.

Required qualifications - Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent practical experience. - 2+ years of hands-on data development in a production environment with scheduled pipelines and real downstream consumers. - Experience owning a production pipeline or component, including when it breaks. - Strong SQL: window functions, complex multi-table joins, incremental and idempotent writes; able to read an execution plan and diagnose data skew or slow queries. - Production-grade Python: maintainable, testable ETL and tooling code (PySpark / pandas / boto3) with proper error handling and logging. - Han