We are hiring a Data Engineer I with 2–3 years of experience to strengthen the Data Engineering team’s operational capacity, support recurring business data needs, and enable the team’s focus on long-term strategic initiatives such as real-time ingestion, data automation, and platform modernization.
Why This Role Matters
Our Data Engineering team operates at the intersection of product, analytics, and platform engineering. As data demands increase, we need dedicated engineers who can build, maintain, and evolve data pipelines and systems to ensure fast, reliable, and secure access to data across teams.
This role is ideal for someone who enjoys working across the stack—solving business problems with data, automating workflows, and continuously improving reliability and performance.
Key Responsibilities
Design, build, and maintain scalable, production-grade data pipelines using Spark and Airflow
Build and optimize queries for large datasets using Trino/Presto and SQL
Develop and manage data workflows on AWS, including services like S3, Glue, Lambda, and EMR
Contribute to initiatives around schema automation, metadata management, and incremental ingestion
Collaborate with cross-functional teams (Product, Analytics, Marketing) to unblock data workflows and support experimentation
Assist in the refactoring and automation of legacy pipelines to improve maintainability and scalability
Contribute to roadmap-driven efforts such as real-time ingestion, analytics platform unification, and data governance tooling
What We’re Looking For
2–3 years of experience in data engineering or related roles
Proficiency in Spark and Python for ETL development
Experience with Airflow for scheduling and orchestration
Solid understanding of distributed query engines like Trino/Presto
Hands-on experience with AWS services for data processing and storage
Ability to write and optimize efficient, scalable SQL
A strong sense of ownership, curiosity, and eagerness to work in a high-growth environment
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