数据摄入资深软件工程师
Staff Software Engineer - Data Ingestion
As a Staff Software Engineer, you will lead the evolution of our backend architecture, with a primary focus on refactoring and optimizing existing data pipelines. You will drive the development of next-generation distributed data storage and processing systems designed to scale indefinitely and surpass traditional query performance. Beyond modernization, you will design clean, expressive interfaces that abstract complexity for a wide range of data consumers—from core web applications to advanced business analytics and AI. Your expertise will be instrumental in transforming our infrastructure into a robust, high-performance foundation.
Primary Duties:
Identify and develop scalable and performant solutions.
Work across discipline to shape product strategy and execution.
Develop the foundations of code architecture and quality.
Mentor and coach engineers.
Set and uphold the standard for engineering processes to support high-quality engineering.
Minimum Qualifications:
BS/BTech (or higher) in Computer Science, Engineering or a related field required.
8+ years of production-level experience as an engineer building highly scalable systems.
4+ years of experience acting as a trusted technical decision-maker in a team setting, solving for short-term and long-term business value.
4+ years of experience working with SQL or other database querying languages on large multi-table data sets.
Experience architecting, developing, and deploying large-scale distributed systems at scale.
Experience with cloud technologies, e.g., AWS, Azure, GCP.
Experience building continuous integration and continuous development (CI/CD) pipelines.
Strong familiarity with server-side web technologies (eg: Java, Python, Scala, C#, C++, Go).
Preferred KSAs:
8+ years experience building highly scalable and reliable infrastructure.
Expertise in designing, optimizing, and orchestrating robust data pipelines (ETL/ELT) and ingestion systems for large-scale, real-time, and batch processing.
Experience managing data warehouses (e.g., Snowflake, Redshift) and leveraging analytics tools (e.g., Spark, SQL, Python, Databricks).
Hands-on experience with containerization (Docker, Kubernetes), CI/CD pipelines, and distributed architectures (event-driven, in-memory computing).
Deep proficiency with modern database systems, including replication, sharding, partitioning, indexing, and caching strategies for high-performance query optimization.
Strong understanding of data security, governance, and compliance principles.
Experience with infrastructure monitoring, performance optimization, and active participation in architecture reviews.
Physical Requirements:
Sitting for prolonged periods of time. Extensive use of computers and keyboard. Occasional walking and lifting may be required.