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.

• 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. 

• 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).

• 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.