Data Engineer - AI / Python / (Banking or Capital Market)

  • New York, NY
  • Posted 13 hours ago | Updated 13 hours ago

Overview

Hybrid
$160,000 - $195,000
Full Time

Skills

Python
AI
Azure
AWS

Job Details

Data Engineer

Full Time

Required Location: Hybrid/ Midtown NYC 3 days a week.

Interview Required: Video

A senior Data Engineer with extensive experience working with Python and AI in a Financial/Capital Markets environment. Candidates will support a Generative AI platform by building scalable data pipelines and systems. This role blends traditional data engineering with modern development practices, including exposure to APIs and microservices for AI/ML integration.

Manager Notes: Project: Develop a comprehensive Gen AI initiative that spans Anurag's entire portfolio, with a focus on integrating AI capabilities into banking and capital markets.

Gen AI Tool Development: They are building a proprietary tool akin to ChatGPT, tailored for RBC's investment banking division.

Target users: Investment bankers.

Use case: Leverage internal banking data to provide intelligent, real-time insights

Please make sure that each submittal includes:

  1. Driver s license or State ID
  2. Link to the candidates LinkedIn account.

Job Description: RBC Capital Markets is hiring a Data Engineer to support a Generative AI platform by building scalable data pipelines and systems. This role blends traditional data engineering with modern development practices, including exposure to APIs and microservices for AI/ML integration.
Hybrid Schedule: In-office 3 days per week

Key Responsibilities:

  • Design and build ETL/ELT pipelines for structured and unstructured data.
  • Develop and optimize data models for analytics and ML workflows.
  • Support API integration and collaborate on lightweight services exposing data assets.
  • Work with data scientists and ML engineers to productionize datasets and features.
  • Ensure data quality, scalability, and performance across systems.

Must-Have:

  • 5+ years of experience in data engineering or backend development.
  • Strong in Python, SQL, and distributed systems (e.g., Spark, Kafka, Airflow).
  • Experience with cloud platforms (AWS preferred) and data lake/data warehouse design.
  • Familiarity with APIs or event-driven architecture is a plus.

Nice-to-Have:

  • Exposure to ML pipelines, feature stores, or AI platforms.
  • Experience in financial services or regulated environments.
  • Understanding of data governance and security best practices.
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