Fulltime Role---Sr. Python Data Engineer in Rutherford, NJ

Role: Sr. Python Data Engineer

Location: Rutherford, NJ

Duration: Fulltime Position

Key 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.

Core Requirements (The Must-Haves):

• 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 Qualifications (What Makes You Stand Out):

• 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).