The job, in the employer's words
About Us:
At Parafin, we’re on a mission to grow small businesses.
Small businesses are the backbone of our economy, but traditional banks often don’t have their backs. We build tech that makes it simple for small businesses to access the financial tools they need through the platforms they already sell on.
We partner with companies like DoorDash, Amazon, Worldpay, and Mindbody to offer fast and flexible funding, spend management, and savings tools to their small business users via a simple integration. Parafin takes on all the complexity of capital markets, underwriting, servicing, compliance, and customer service for our partners.
We’re a tight-knit team of innovators hailing from Stripe, Square, Plaid, Coinbase, Robinhood, CERN, and more — all united by a passion for building tools that help small businesses succeed. Parafin is backed by prominent venture capitalists including GIC, Notable Capital, Redpoint Ventures, Ribbit Capital, and Thrive Capital. Parafin is a Series C company, and we have raised more than $194M in equity and $340M in debt facilities.
Join us in creating a future where every small business has the financial tools they need.
About the Position:
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.
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
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.
What We’re Looking For:
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.
Bonus Points
Experience with Databricks tech ecosystem
Experience at startups
Experience in the fintech domain
What We Offer:
Salary Range: $290k - $330k
Equity grant
Medical, dental & vision insurance
Work from home flexibility
Unlimited PTO
Commuter benefits
Free lunches
Paid parental leave
401(k)
Employee assistance program
If you require reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please contact us.
Benefits mentioned
- Salary Range: $290k - $330k
- Equity grant
- Medical, dental & vision insurance
- Work from home flexibility
- Unlimited PTO
- Commuter benefits
- Free lunches
- Paid parental leave
- 401(k)
- Employee assistance program
Requirements mentioned
- 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.
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