About Greenline

At Greenline, we're dedicated to unlocking mortgage lending for self-employed and non-traditional borrowers.

We’re a small and nimble team that values brains, empathy, and hustle. Our team operates remotely, and we’ll give preference to candidates in the SF Bay Area, Austin, and NYC.

Greenline’s engineers are involved throughout the product lifecycle, from idea generation and design to prototyping and production delivery. You will partner closely with engineering and non-technical teammates to understand our customers' problems and ship solutions accordingly. You’ll have high agency and a strong bias toward action.

Job Description

Greenline’s founding backend and data platform engineer will transform how lenders and related parties use our insights.

This is a senior- /staff-level individual contributor role with architectural influence. You will design resilient data pipelines and collaborate with product and compliance stakeholders to ensure correctness, auditability, and extensibility.

This is not a research role. Rather, this is an underdeveloped area at Greenline for you to shape and own, which will include:

• Core Infrastructure - build the foundational platform for Greenline products and services to operate at scale. We drive reliability, availability, efficiency, and scalability of these systems. You’ll collaborate on a mix of platform, infrastructure, and product problems to ensure Greenline thrives in a heavily regulated industry.

• Data Integrations & Platform – ingestion, normalization, and analysis of third-party financial data from external data sources, including:

  • Banking and asset data (Plaid, Finicity, MX)
  • Tax and income data (IRS transcripts, W-2/ 1099 sources)
  • Accounting platforms (QuickBooks, Xero, similar systems)

  You’ll own the data-serving infrastructure, including:

  • Authentication and authorization
  • Webhooks, de-duplication, and backfill strategies
  • Rate limiting, retries, idempotency, and error classification
  • Design integration layers that isolate vendor-specific behavior and support future provider swaps.
  • Data normalization & canonical modeling

• Analytics & Applied ML Enablement – develop feature pipelines, validation, and productionizing signals.

  • Implement feature extraction pipelines suitable for downstream analytics and ML usage
  • Develop in-house and apply off-the-shelf ML models and services (e.g., BERT, classification, clustering, anomaly detection)
  • Collaborate with product and business stakeholders to translate model outputs into actionable insights

Responsibilities

• Collaborate with cross-functional teams to develop and implement machine learning models, algorithms, and ledger-based technologies.
• Build performant and scalable systems for storage, auth, or asset serving to enable other product teams to build robust applications.
• Design relational database schemas in Postgres and manage SQL migrations.
• Maintain and evolve testing pipelines, Docker builds, and rollout processes for backend services, leveraging blue/green or canary deployments as needed.

Must-Have Skills

• Experience designing and operating:
  • Data ingestion pipelines
  • Event-driven or batch processing systems
  • APIs that support downstream analytics
• Deep understanding of relational databases and data modeling (Postgres strongly preferred)
• Demonstrated ability to reason about:
  • Data correctness and reconciliation
  • Auditability and traceability
  • Long-lived data contracts