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In 2024, the Data Platform Engineering team is going to implement various data processing tasks on the newly built next-generation data platform. We aim to further optimize data storage, processing, application, interfaces, and other dimensions. Throughout the continuous iteration process, we will also incorporate new data components and consider new data architectures (such as Lake House) to achieve faster data scheduling, significantly reducing data latency and enabling more efficient operation of data engineering applications.
Job Responsibility:
Design, build and maintain scalable and reliable batch and stream data pipelines for internal analytics and operational use cases
Participate in building data ingestion frameworks connecting internal systems with the centralized data platform using tools like Kafka, Flink, or Spark
Collaborate with senior engineers to define and maintain internal data engineering tools, standards, and practices
Contribute to automation and reliability improvements that reduce data latency and improve platform scalability
Assist with integration of external (third-party) data sources and handle data quality and consistency concerns
Work cross-functionally with product, engineering, and BI teams to translate business requirements into engineering solutions
Requirements:
3+ years of relevant experience as a data engineer
Bachelor's or Master's degree in CS/CE/CIS (or equivalent experience)
Knowledge of Kotlin / Java / Scala / Python / SQL
Knowledge of Kafka, Data Lake, Spark, Flink, DBT, Spring Boot, Hadoop, BigQuery, and Snowflake is preferred
Ability to work with senior/leader data platform engineers to design, build, and operate distributed systems and establishing overarching data architecture
Possess strong working knowledge of Real-time/Batch processing systems
Experience with working data at PB scale
What we offer:
Competitive salary plus valuable equity
Collaborative open office space with a fully stocked kitchen