Data/AI engineer with Python

Project description

Build reusable platform capabilities and data products that let quantitative researchers discover, access, and consume data across a complex, disparate landscape, and then accelerate the good prototypes to production.

Responsibilities

• - Design, build, and enhance data pipelines and integration frameworks supporting AI, analytics, and business applications. - Improve data quality, reliability, lineage, and monitoring. - Implement AI first approach: use AI agents as a primary tool to design, build, test, and support solutions - Collaborate closely with business stakeholders, product managers, and engineering teams to translate business objectives into technical solutions.

SKILLS

Must have

• - 10+ years of professional software development, including 5+ years of data engineering experience. - Advanced proficiency in Python, including experience building scalable backend services and data processing solutions. - Strong hands-on experience designing and implementing data pipelines, data integration solutions, with a latency and cost in mind - Experience working with relational databases and modern data storage technologies, including graph, vector, or analytical data platforms. - Experience with data quality and data governance approaches, including validations in the pipeline, entitlements, audit, and lineage - Experience operating in agile delivery environments. - Strong communication skills and ability to interact efficiently with various stakeholder groups including business users. AI Acumen
• Experience applying modern agentic approaches (spec-driven development) to software engineering
• Hands-on with Claude Code, VS Code + Copilot, and building or using reusable Al skills
• Al-first repositories - build agent-ready repos - context, skills, conventions & evals baked in
• Quality - agent-generated test & regression suites; identify and guide agents to build quality unit and system test cases

Nice to have

- Highly desirable: financial services, specifically fixed income or trading domains experience - Experience with Databricks, Spark, Delta Lake, or similar modern data engineering platforms. - Experience designing enterprise data lakes, lakehouses, or large-scale analytical platforms. - Experience with Aurora, Neptune, and other AWS data services. Strong hands-on experience designing and implementing data pipelines, data integration solutions, and large-scale data processing architectures. - Experience architecting, provisioning, and managing production infrastructure on AWS, including services such as ECS Fargate, Lambda, SQS, and related cloud-native technologies. - Experience defining cloud architecture programmatically using Infrastructure-as-Code (IaC) tools such as AWS CDK or Terraform.