RESPONSIBILITIES:

• Play a lead role in every major facet of engineering (e.g., architecture, best practices, technical vision) 
• Own major technical initiatives for the Cash Platform team and across the engineering organization
• Work closely with stakeholders to scope out technical projects and delegate large projects across the organization
• Actively lead in standard engineering activities like technical design, code reviews, on-call support, documentation, and sprint ceremonies.
• Mentor engineers from junior to senior
• Collaborate with product and data science teams to evolve our ML-based cashflow data platform, powering our "income navigator" and "cashflow underwriting" products.
• Collaborate with the data scientists 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.
• Contribute to data observability strategy and implementation 
• Develop quality controls around our ML-powered services to ensure model effectiveness
• Devise and drive tooling and framework improvements to improve R&D outcomes

REQUIREMENTS:

• 8+ years of software engineering experience
• 3+ years driving large multi-team software projects from problem statement to ongoing maintenance
• 5+ years experience with databases, data warehouses, or data lakes; strong SQL skills
• Advanced experience and knowledge of data engineering tools like Spark, Hadoop, Pandas, or Airflow
• Experience leveraging common observability tools like Grafana, Kibana, or Datadog
• Proficient in Python; experience writing idiomatic (Pythonic) code and building Python-based web applications (Flask, FastAPI, Django)
• Knowledge of numerical libraries (numpy, pandas, SciPy, polars, etc.)
• Ability to design sound, highly performant solutions in AWS; articulate design decisions and tradeoffs well.
• Strong grasp of existing and emerging software testing methods
• 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
• Set and drive observability best practices and standards to support stakeholder decision-making and operational use cases.
• Design and drive the build of an internal MLOps platform to accelerate R&D cycle times and enhance monitoring.
• Pair with data scientists to write state-of-the-art quantitative cashflow models using computing frameworks like PySpark, Dask, Ray, or Polars.
• Measure & optimize data pipeline latency to shorten overall response times

YOU SHOULD ESPECIALLY APPLY IF:

• You want to have a significant impact on your next company
• You feel comfortable tackling big ambiguous problems and delegating in a multi-team setting
• You go the extra mile not just in your work but in your review of others’ work
• You’re quick to take the lead when conversations and projects get stuck
• Exposure to MLOps (e.g., MLflow, Sagemaker, Databricks)
• Experience with banking data or fintech experience