Senior Data Engineer

Firstup•Chicago, IL

10d•Remote

About The Position

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.

Requirements

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.

Nice To Haves

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.

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.