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.

Responsibilities

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.

Requirements

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).; Sitting for prolonged periods of time. Extensive use of computers and keyboard. Occasional walking and lifting may be required.

Nice to have

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.