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.

Other

Languages

English: B2 Upper Intermediate

Seniority

Senior

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