Staff Software Engineer- Data Ingestion

Job details

Work model

Remote

Salary

Not listed by source

Job description

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.

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