Engineering Manager, Data Platform

The Data Platform team owns the systems that generate every capital product offer at Parafin, powering both batch and real-time underwriting across all of our partners and products.

We're looking for an engineering manager who is still deeply technical, closer to a tech lead manager. You'll set technical direction for the team, and manage and grow a team of 5-6 engineers across three tightly coupled areas: Data Platform (warehouse, ingestion, Airflow/Databricks infra), Feature Store & ML Platform (feature materialization, model training/serving lifecycle), and Underwriting Platform (batch and real-time pipelines that generate offers).

This is a high-leverage role: every improvement to platform reliability or iteration speed compounds across 60+ partners and every product line. You'll also be a primary technical partner to Underwriting Data Science that owns model research, risk analysis, and new product development, as well as to product engineering teams that build features on top of the data and underwriting systems this team owns.

Manage and grow a team of 5-6 engineers spanning Data storage and schema, Feature Store/ML Platform, and Underwriting Platform, including hiring, mentorship, and career development.

Set and drive execution and technical strategy for the team's three areas: warehouse/data infrastructure (Databricks, Airflow, dbt), the feature store and ML dev lifecycle (feature materialization, training, batch/real-time inference, model registry), and underwriting pipelines (nightly batch underwriter, real-time Kitchen RTU).

Stay hands-on: review designs and code, unblock the team on hard technical problems, and personally drive architecture on the highest-priority initiatives.

Own reliability and on-call for the team’s systems; build sustainable processes around incident response, SLAs, and recoverability

Partner closely with underwriting data science, and product engineering teams that build on top of the data platform

Represent the team's roadmap and tradeoffs to cross-functional stakeholders (DS, Merchant Decisioning, Product, Risk) and prioritize across data infra, ML platform, and underwriting pipeline needs.