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# Senior Software Engineer - Cash flow Platform

- **Company:** Nova Credit
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $193,500 - $236,500
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Code Review, Databases, Data Warehousing, Database Queries, Distributed Systems, Django Web Framework, Apache Hadoop, Python (Programming Language), Machine Learning, NumPy, Software Tools, SciPy, Software Engineering, Strategies of Testing, Web Applications, Datadog, Flask (Web Framework), Grafana, Apache Spark, Parallel Computation, Fastapi, Pandas, Data Lakes, Pyspark, Dask, Machine Learning Operations, Kibana, Data Pipelines, Databricks
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_e9efa240831c6fc9f6952c6552d30818

## About the Role

* 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

## Description

* 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., * 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