About The Role

ABOUT THE ROLE

You'll lead the build of a real-time data platform, data warehouse, and infrastructure powering a suite of customer-facing data products at a seed-stage B2B SaaS company. Sitting within the data team, you'll partner closely with data science, product, design, and engineering to lay the data foundation behind analytics and ML models that executives in wholesale distribution rely on daily.

WHAT YOU'LL DO

Design and own end-to-end ETL/ELT pipelines, building flexible schemas that absorb data from many heterogeneous sources.
Carry out data modeling that powers real-time, customer-facing analytics and data products.
Write and deploy high-performance transformations within the data warehouse using modern tooling.
Partner with data consumers across the company to understand usage patterns and design intuitive data models.
Own and configure the data platform (Databricks), working closely with product and engineering to ship data products the team actually uses.
Shape the architecture of the data platform, warehouse, and infrastructure as much of it is still being built.

WHAT WE'RE LOOKING FOR

3+ years of hands-on data engineering experience building and owning ETL/ELT pipelines end to end.
Strong proficiency in SQL and data transformation workflows (dbt or equivalent).
Hands-on experience with cloud data warehouses such as Databricks, Snowflake, or similar.
Solid grounding in software engineering practices applied to data engineering — version control, testing, and deployment patterns.
Experience designing flexible schemas to ingest data from multiple sources and implementing data models for analytics use cases.
Comfort preparing business aggregates and translating stakeholder requirements into data models.
Experience at a high-growth tech startup, with an experimental, launch-fast-and-iterate mindset.
Familiarity with Python, PySpark, Terraform, and AWS is a strong plus.