Fully remote. US-based preferred. If international, significant overlap with US working hours is required.

About TYB

TYB is an AI-powered community commerce platform connecting brands with their most passionate fans through challenges, rewards, and social features. We're Series A and moving fast across a dual-sided marketplace that serves both consumers and brand partners.

The role

We're hiring a Data Engineer to build and own the data infrastructure that powers TYB's analytics, product, and growth decisions. You'll work at the intersection of data engineering and backend development: most of your time will go toward pipelines, warehousing, and data modeling, but you'll also build and maintain the integrations that move data in and out of TYB.

This is a foundational hire on the data side. You'll have real ownership over how data flows through the company, a lot of autonomy in a 0-1 environment where process is still being built, and direct influence over the architecture decisions that the rest of the team builds on top of.

We build with AI, and we expect you to as well — using AI to move faster in your own workflow (spec writing, debugging, pipeline design, documentation) and thinking about where automation and AI-assisted tooling can reduce manual data work across the org.

What you'll do

• Design, build, and maintain reliable ELT/ETL pipelines using Airflow, dbt, and SQL
• Own our data warehouse — modeling, performance, cost, and data quality
• Build and maintain streaming data pipelines (RabbitMQ / Kafka) for real-time and near-real-time use cases
• Develop integrations for data import and export with third-party platforms (e.g., Shopify, Braze, and other partner or vendor APIs)
• Write production backend code in Python and/or TypeScript to support data ingestion, transformation, and integration work
• Work across our AWS environment, including Redshift, RDS/Aurora (Postgres) and S3, to keep data infrastructure scalable and cost-efficient
• Implement testing, monitoring, and documentation practices so data is trustworthy and issues surface early
• Organize and expose curated datasets to support AI and ML features across the product, in close collaboration with the teams building on top of them
• Partner with product, engineering, analytics and business stakeholders to understand data needs and translate them into pipelines and models
• Make architecture and tooling decisions as an early data hire, with room to shape standards as the team grows

What we're looking for

• 5+ years of experience as a data engineer, or in a hybrid data engineering / backend role