Sierra is creating a platform to help businesses build better, more human customer experiences with AI. The job is to build systems making AI agents measurably smarter with every interaction, building real-time pipelines, analytics products, and deep personalization primitives that turn millions of conversations into business outcomes.
Requirements
- You have designed, built, and operated large-scale data systems, processing terabytes or petabytes of data with technologies like Spark, Flink, Clickhouse, Trino/Presto, and data lakehouse formats like Iceberg or Hudi.
- You possess strong backend and distributed systems fundamentals in a language like Go, Scala, or Python.
- You understand the trade-offs of different storage engines, query plans, and data serialization formats.
- You have a history of implementing pragmatic data governance, lineage, and testing.
- You've built platforms that are cost-aware, highly operable, and a delight for other engineers to build upon.
- You're well-versed in data modeling best practices (dimensional modeling, star/snowflake schemas, slowly changing dimensions) and know how to optimize analytical query patterns for both traditional OLAP systems and modern cloud data warehouses.
- You have a portfolio of shipped data products, including analytics dashboards, data visualization tools, or alerting and exploration systems.
Responsibilities
- Architect and build our core platform to handle data at scale with low latency.
- Build the real-time eventing infrastructure, streaming and batch ETL pipelines, and our Iceberg-based data lakehouse.
- Own the systems for interactive OLAP querying, orchestration, and experimentation, ensuring our data is trustworthy, fast, and easy to use for the entire organization.
- Build the products that allow our customers to explore conversational data, diagnose agent behavior, and receive automated suggestions for improving performance.
- Surface key moments across millions of conversations and automatically recommend changes that directly improve critical business metrics, such as containment rate.
- Designed, built, and operated large-scale data systems, processing terabytes or petabytes of data with technologies like Spark, Flink, Clickhouse, Trino/Presto, and data lakehouse formats like Iceberg or Hudi.
- Built platforms that are cost-aware, highly operable, and a delight for other engineers to build upon.
Other
- Degree in Computer Science or related field, or equivalent professional experience.
- Strong software engineering background with 4-7+ years of hands-on development experience in building and shipping production systems or products.
- A passion for being on the frontier of AI products.
- High agency and a bias to action in a high-autonomy environment.
- Deep experience with event streaming platforms (e.g., Kafka, Kinesis) and real-time data processing.
