Senior Data Engineers

About VIDAA

VIDAA is a leading TV OS distributor, pioneering the next-generation platform-to-the-home. Starting with a Smart TV OS, we aim to encompass various aspects of consumers' digital and connected lives. With millions of households reached daily, we are looking for people that want to drive excellence and innovation.

We are a diverse and passionate company looking for the best and the brightest people to lead sucees. We value people and their needs and we provide a safe and secure environment for everyone. If you are self-motivated, entrepreneurial and know how to thrive in a fast growing environment, then we should talk.

Job Responsibilities

1. Own the construction and maintenance of the company’s business data ingestion system. Responsible for the full lifecycle development of real-time collection, cleaning, and warehousing of business logs, user behavioral data, and database operational data, ensuring the completeness, timeliness, and stability of end-to-end data ingestion pipelines.

2. Lead the implementation and iterative optimization of the real-time data warehouse architecture. Design multi-layer real-time data models, develop and tune streaming ETL pipelines, and support the stable output of real-time metrics, dashboards, and business monitoring data.

3. Collaborate closely with business teams, data analysts, and data product managers. Fully understand business logic and statistical calibers, independently complete data requirement decomposition, logical sorting, development implementation, release verification, and continuous iteration.

4. Maintain and optimize Kafka streaming pipelines for real-time data ingestion and consumption. Troubleshoot and resolve core online issues including message backlog, data skew, data loss and duplication, and ensure high availability and stability of real-time data pipelines.

5. Develop, tune, and operate Spark real-time tasks. Continuously optimize computing resource utilization, data latency, and throughput, and improve the overall performance and stability of streaming data pipelines.

6. Participate in data standardization construction, real-time data quality governance, and pipeline redundancy optimization. Summarize and standardize data ingestion specifications and development best practices to improve the overall team’s data development efficiency.

7. Provide stable real-time data support for downstream business scenarios including BI dashboards, data services, user portrait platforms, and business effectiveness measurement, ensuring reliable online data service delivery.

Job Requirements

1. Bachelor’s degree or above in Computer Science, Software Engineering, Data Science, or related majors. 3+ years of professional big data development experience in the internet industry, with proven experience in end-to-end real-time data warehouse implementation and data ingestion system construction.

2. Proficient in English reading, writing and verbal communication skills, capable of collaborating with overseas teams and business stakeholders. Overseas study or work experience is highly preferred.

3. Expert-level knowledge of Kafka. Familiar with real-time data ingestion, partition strategy, consumer mechanism, and capable of diagnosing and optimizing online risks such as message backlog, data skew, data loss and duplication; experienced in large-scale streaming pipeline stability governance.

4. Hands-on experience with Spark, Spark SQL, Flink and Flink SQL. Solid capabilities in streaming task development, resource tuning, latency and throughput optimization, and online troubleshooting. Familiar with real-time data warehouse layered architecture and dimensional modeling methodologies.

5. Deep understanding of the big data ingestion system. Experienced in full-process real-time processing of multi-source data, including user behavior logs and business database data, proficient in real-time collection, CDC synchronization, data cleaning, and exception handling with standardized implementation capabilities.

6. Practical experience with cloud-native data tools including BigQuery (BQ), DBT and Databricks. Capable of data warehouse modeling, pipeline orchestration, metric iteration and dashboard development in cloud data environments.

7. Proficient in at least one programming language among Python, Scala and Java with solid hands-on big data development capabilities. Familiar with Linux commands and Shell scripting, able to independently complete task deployment, log analysis and daily operation maintenance.

8. Familiar with mainstream relational databases and Binlog/CDC real-time synchronization principles. Experienced in business database real-time ingestion implementation. Knowledgeable in real-time data SLA governance, data quality monitoring, alert configuration and incident review mechanisms.

9. Experience with Google Cloud, Azure or other mainstream cloud big data platforms, as well as overseas team collaboration and real-time data governance experience is highly preferred.