Role: Python - Data Engineer

Type: Full-Time

Locations: Rutherford, NJ

Our challenge

We are seeking an elite Python- Data Engineer to join a high-impact team at the forefront of technological evolution. This is not typical enterprise role. We operate with the agility and innovation of a startup, tackling some of the most complex challenges in the financial industry. Candidate will be instrumental in architecting and building a sophisticated knowledge graph, leveraging Generative AI to revolutionize how we understand and manage credit risk. If candidate thrive on solving complex problems, building scalable systems from the ground up, and working with the latest technologies.

Responsibilities:

• Architect & Build: Design, develop, and deploy robust, production-grade data pipelines to extract and process vast amounts of structured and unstructured financial data.

• Innovate with AI: Pioneer the use of LLMs and Generative AI to clean, enrich, and analyze data, building the foundational layers of our financial knowledge graph.

• Model & Deploy: Engineer and productionize predictive and prescriptive models, collaborating closely with quant and business teams to ensure they deliver tangible value in our live environment.

• Solve Complex Problems: Dive deep into intricate financial datasets, with a specific focus on credit risk, to identify patterns, build insights, and create innovative solutions.

• Collaborate & Drive: Act as a key technical partner to business and technology leaders, translating complex requirements into scalable, resilient, and high-performance systems.

• Learn & Adapt: Maintain an open and adaptive mindset, continuously exploring new advancements in LLMs, GenAI, and data engineering to drive innovation within the team.

Requirements:

• Experience level 8+ years, Expert-Level Python: Deep, hands-on proficiency with modern Python (3.11+).

• Modern Frameworks: Proven experience building high-performance, production-ready services and data models using the latest Python frameworks, including FastAPI and Pydantic.

• Data Tooling: Strong command of core data manipulation and analysis libraries (e.g., Pandas, NumPy, Polars).

• Database Proficiency: Advanced SQL skills and extensive experience working with large-scale relational databases (e.g., Sybase IQ, PostgreSQL, Oracle).

• Educational Foundation: Bachelor's degree in Computer Science, Engineering, or a related quantitative field (or equivalent practical experience).

• Problem-Solving Mindset: A proven ability to dissect complex, often ambiguous problems and engineer elegant, effective solutions.

Preferred, but not required:

• Graph Technology: Practical experience with graph databases, specifically Neo4j Enterprise, and graph data modeling concepts.

• Diverse Database Experience: Proficiency with various database systems, including relational databases like PostgreSQL and NoSQL databases like MongoDB.

• GenAI & LLM Experience: Hands-on experience with modern AI frameworks like LangChain, LlamaIndex, or Hugging Face Transformers.

• Big Data Expertise: Familiarity with distributed computing frameworks like Apache Spark (PySpark) or Dask.

• Financial Domain Knowledge: Prior experience in the financial services industry, especially within risk management, is a significant plus.

• MLOps: Understanding of MLOps principles and tools for model versioning, deployment, and monitoring (e.g., MLflow, Kubeflow).