Senior Data Engineer

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.

Responsibilities

• 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.

Minimum Qualifications

• A 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.

Preferred Qualifications

• 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.