Software Engineer, Sector Data Science

Thurn Partners; New York (NY); On-site; USD 120,000 - 190,000; Full time

Job summary

Thurn Partners is building out its sector data science capability in New York. You will own data products end to end—from ingestion and pipelines to the tools investment teams rely on—while shaping scalable frameworks and automation.

Ideal candidates ship data-intensive products, design data pipelines and APIs, and thrive turning messy data into production-ready sources. A product mindset and curiosity about markets are essential.

Qualifications

• Strong software engineering fundamentals, with advanced Python and strong SQL.
• Track record of building and shipping data-intensive products, ideally as an early or founding engineer.
• Experience designing data pipelines, APIs, and scalable systems, and automating manual workflows.
• Comfort with large, messy, unstructured data and the full lifecycle of getting it production-ready.
• A product mindset: you talk to users, prioritize ruthlessly, and communicate clearly with non-engineers.
• Intellectual curiosity about markets and investing (no finance background required).
• Bachelor's degree in computer science, engineering, or a related field, or equivalent hands-on experience.
• Bonus: cloud data infrastructure, orchestration (Airflow, Dagster, Prefect), or ML tooling.

Responsibilities

• Own data products end to end: ingestion, pipelines, transformation, storage, and the interfaces investors use.
• Build scalable frameworks and automation that turn recurring, manual research requests into self-serving tools, so maintenance never swallows the roadmap.
• Wrangle large, complex, and unstructured datasets into clean, queryable, well-documented sources.
• Partner with investment teams to understand what they need, ship pragmatic solutions fast, then iterate.
• Use LLMs and modern AI infrastructure to build agentic tooling that extracts insight at scale.
• Bring engineering discipline (testing, versioning, monitoring) to a fast-moving research environment.

Skills

Advanced Python

Strong SQL

Software engineering fundamentals

Education

Bachelor's degree in computer science or engineering or related field

Tools

Airflow

Dagster

Prefect

ML tooling

Job description

A leading global investment firm is building out its sector data science capability and needs engineers who can turn messy, large-scale data into reliable, reusable systems that investors depend on. You will not be maintaining someone else's stack. You will be in the build phase, owning data products end to end, from ingestion and pipelines through to the tools and frameworks investment teams use every day.

This is a rare seat for a commercial software engineer who wants their work to have immediate, measurable impact, and who is curious about markets even without a finance background. If you have been an early engineer at a startup, shipped data-heavy products, and automated your way out of repetitive work, this is that same instinct applied to investing.

Your Role:

• Own data products end to end: ingestion, pipelines, transformation, storage, and the interfaces investors use


• Build scalable frameworks and automation that turn recurring, manual research requests into self-serving tools, so maintenance never swallows the roadmap


• Wrangle large, complex, and unstructured datasets into clean, queryable, well-documented sources


• Partner with investment teams to understand what they need, ship pragmatic solutions fast, then iterate


• Use LLMs and modern AI infrastructure to build agentic tooling that extracts insight at scale


• Bring engineering discipline (testing, versioning, monitoring) to a fast-moving research environment

Experience/Skills Required:

• Strong software engineering fundamentals, with advanced Python and strong SQL


• Track record of building and shipping data-intensive products, ideally as an early or founding engineer


• Experience designing data pipelines, APIs, and scalable systems, and automating manual workflows


• Comfort with large, messy, unstructured data and the full lifecycle of getting it production-ready


• A product mindset: you talk to users, prioritize ruthlessly, and communicate clearly with non-engineers


• Intellectual curiosity about markets and investing (no finance background required)


• Bachelor's degree in computer science, engineering, or a related field, or equivalent hands-on experience


• Bonus: cloud data infrastructure, orchestration (Airflow, Dagster, Prefect), or ML tooling

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