Engineering Manager, Data Platform - parafin

WHAT YOU'LL DO

• 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.

• 5+ years of software engineering experience, including 2+ years in a technical leadership or engineering management role, ideally in data infrastructure, ML platform, or a similar backend/data domain.
• Ability to stay close to the team’s execution, review/discuss technical designs and trade-offs
• Strong understanding of modern data/lakehouse stacks:
  Spark/PySpark, Databricks, Airflow, and cloud infra (AWS).
• Experience with ML infrastructure concepts: feature stores, model training/serving pipelines, model registries, batch and real-time inference.
• Track record of managing engineers with empathy, giving direct feedback, and building a healthy, high-ownership team culture.
• Experience building strong teams by hiring to a high technical bar and actively growing engineers' careers.
• Experience navigating ambiguity and competing priorities across multiple technical domains and stakeholders; strong judgment around reliability and operational rigor
• Excellent written and verbal communication; comfortable representing technical tradeoffs to both engineers and non-technical stakeholders.

• Experience with Databricks tech ecosystem
• Experience at startups
• Experience in the fintech domain