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Remote, Oregon, United States
Remote only
The Senior Data Engineer will design and build reliable data solutions that translate complex product and business logic into scalable, well-tested data models and pipelines. This role requires deep expertise in SQL, query acceleration, and applying GenAI to both engineering workflows and data/analytics interfaces. They will partner across engineering, analytics, and business teams, contribute to shared codebases, improve data quality and observability, and help evolve the data architecture.
Design, build, and maintain scalable data pipelines, warehouse models, and analytics solutions, balancing data quality, business value, and speed.
Build and maintain natural language interfaces to data and analytics, applying GenAI/LLM techniques to make data more accessible across the business.
Use GenAI coding tools and practices in daily development to improve code quality, testing, and delivery speed.
Continuously evaluate new technologies that could improve and scale the team's data platform and technology stack.
Establish and follow standards for SQL development, data modeling, testing, documentation, code reviews, and production support.
Support production data pipelines through on-call rotation and incident response, partnering with engineering, analytics, and business teams.
Document and maintain expertise in the technology stack and product domain, translating business needs into technical solutions with product management.
Bachelor's degree in Computer Science or related field, or equivalent professional experience.
8+ years building reliable, high-performance, large-scale distributed systems, with an emphasis on streaming and data pipelines.
Experience working with and maintaining multi-tenant SaaS experiences.
Experience building natural language interfaces over data warehouses, including applying GenAI/LLM techniques to data and analytics.
Enterprise-level experience with at least one large-scale analytical data warehouse or query engine: StarRocks, Amazon Redshift, Snowflake, Databricks, or Trino.
Expertise writing, optimizing, and analyzing SQL.
Hands-on experience building and operating distributed data platforms on AWS or GCP.
Hands-on experience with streaming platforms such as Kafka and Spark.
Experience scaling data modeling and warehousing.
Proficiency in python.
Experience with Cube or other semantic layers.
Experience with scheduling tools such as Airflow.
Familiarity with the BI tool Metabase.
Proficiency in Ruby on Rails, React, or other adjacent languages.
