Senior Software Engineer - Cash flow Platform

RESPONSIBILITIES:

• Collaborate with the product and data science teams to build our cashflow data platform, powering our "verification of income" and "cashflow underwriting" solutions.
• Collaborate with the data science team to productionalize research models; this may include supplementing research-driven models with additional considerations such as unit processing efficiency, batch efficiency, and parallel computing use cases.
• Increase data observability for customers and stakeholders
• Develop quality controls around our ML-powered services to ensure model effectiveness
• Maintain and improve production ML models such as algorithmic calculations, regression models, and classifiers.
• Develop tooling and frameworks to improve research velocity and outcomes
• Participate in standard engineering activities like technical design, code reviews, on-call support, documentation, and sprint ceremonies.

REQUIREMENTS:

• 5+ years of software engineering experience
• 2+ years driving large multi-team software projects from problem statement to ongoing maintenance
• 3+ years experience with databases, data warehouses, or data lakes; strong SQL skills
• 2+ years using data engineering tools like Spark, Hadoop, Pandas, or Airflow
• Proficient in Python; experience writing idiomatic (Pythonic) code
• Experience building Python-based web applications (Flask, FastAPI, Django)
• Deep understanding of a popular distributed computing framework like Spark, Databricks, Ray, Airflow, Sagemaker, or AWS Batch
• Knowledge of numerical libraries (numpy, pandas, SciPy, et. c)
• Experience in observability tools like Grafana, Kibana, or Datadog
• Ability to design sound, highly performant solutions in AWS; articulate design decisions and tradeoffs
• Firm grasp of software testing methodologies
• Curious, self-driven, and eager to learn

SAMPLE PROJECTS:

• Build a unified data platform capable of seamlessly processing financial data payloads like bank accounts/transactions, payroll information, or OCR-driven financial document extractions.
• Design an event-based system for monitoring production ML models
• Create an operations dashboard that articulates the customers and stakeholders
• Stand up an MLOps platform to test model changes against historical data
• Pair with data scientists to write state-of-the-art quantitative cashflow models using computing frameworks like PySpark, Dask, Ray, or Polars.
• Optimize data pipeline latency to shorten overall response time

YOU SHOULD ESPECIALLY APPLY IF:

• You want to make a significant impact on your next company
• Going the extra mile to execute well is the norm for you
• You're quick to take the lead when conversations and projects require refinement
• You've been exposed to MLOps (e.g., MLflow, Sagemaker, Databricks)
• You have experience with banking data or working within a FinTech