Data Engineer Manager, Alternative Data

Long Ridge Partners; New York (NY); Hybrid; USD 500,000 - 800,000; Full time

Job summary

Long Ridge Partners is seeking a Data Engineering Manager to lead its Alternative Data engineering function. This player-coach role balances strategic direction with hands-on design, delivering trustworthy datasets and robust pipelines for quantitative research.

You'll manage a team of five, partner with data scientists, and translate business questions into scalable technical solutions using Python, SQL, Snowflake or Databricks, dbt, and Dagster. Hybrid NYC-based work.

Qualifications

• 8+ years building software, data platforms, or analytics platforms.
• 3+ years leading engineering teams, including managers.
• A degree in Computer Science or related field with a strong academic record.
• A track record of setting technical strategy, building roadmaps, aligning stakeholders, and leading teams through ambiguity.
• Deep proficiency in Python and SQL, with strong fundamentals in data modeling, data warehousing, distributed compute, and APIs.
• Hands-on experience with dbt, cloud data warehouses (Snowflake or Databricks), and orchestration tools such as Dagster.
• Solid experience working in a cloud environment (AWS, Azure, or GCP).
• Strong problem-solving judgment and the ability to communicate and influence across both technical and non-technical audiences.
• A genuine partnership mindset with data scientists, and the ability to translate analytical needs into robust technical solutions.
• Strong people leadership skills, including hiring, coaching, performance management, and building a high-accountability engineering culture.
• Experience applying large language models or AI-assisted development tools to improve engineering or analytical workflows.

Responsibilities

• Define and drive the technical vision and roadmap for data engineering and analytical tooling in support of the data science team, including prioritization, technical direction, and stakeholder communication
• Lead, coach, and support a team of data and software engineers while staying deeply hands-on in design and implementation
• Design, build, and maintain data pipelines, Python packages, analytical tools, frameworks, and vendor platform integrations that power investment research
• Own the full pipeline lifecycle: requirements gathering, orchestration, transformation, validation, observability, documentation, and ongoing support
• Communicate technical strategy, delivery progress, risks, and tradeoffs clearly to both technical and business stakeholders
• Run code reviews and lead discussions on architecture, systems design, data modeling, and technical standards
• Champion engineering best practices, including automated testing, CI/CD, documentation, maintainability, and reusable design
• Prioritize ruthlessly, balancing quick tactical fixes with longer-term, scalable solutions

Skills

Python

SQL

Data modeling

Data warehousing

Distributed compute

APIs

Leadership

Stakeholder communication

CI/CD

Cloud platforms

Education

Bachelor's degree in Computer Science or related field

Tools

dbt

Snowflake

Databricks

Dagster

Spark

ETL tooling

Job description

Hybrid | NYC

Total Compensation: $500,000 - 800,000+

About the Opportunity

A top-tier hedge fund with a multi-decade track record and over $55 billions in assets under management is looking for a Data Engineering Manager to lead its Alternative Data engineering function. This firm runs a purely fundamental, long/short equity strategy, and over the past several years has been deliberately building out quantitative and engineering capability to support its investment team, including growing dedicated data engineering and data science functions.

This is a player-coach role

you'll set technical direction for a small, senior team while staying hands-on in design and implementation roughly half the time. The team's mission is to help data scientists influence investment decisions faster and more reliably, building trustworthy datasets, robust pipelines, self-service Python libraries, and vendor integrations that the research organization relies on daily. you'll work on things that feeds directly into how the fund evaluates alternative data as a predictor of company-level KPIs.

You'll manage a team of five and you will report into a growing quantitative infrastructure organization and partner closely with data scientists and researchers to translate business questions into well-architected technical solutions.

What You'll Do

• Define and drive the technical vision and roadmap for data engineering and analytical tooling in support of the data science team, including prioritization, technical direction, and stakeholder communication
• Lead, coach, and support a team of data and software engineers while staying deeply hands-on in design and implementation
• Design, build, and maintain data pipelines, Python packages, analytical tools, frameworks, and vendor platform integrations that power investment research
• Own the full pipeline lifecycle: requirements gathering, orchestration, transformation, validation, observability, documentation, and ongoing support
• Communicate technical strategy, delivery progress, risks, and tradeoffs clearly to both technical and business stakeholders
• Run code reviews and lead discussions on architecture, systems design, data modeling, and technical standards
• Champion engineering best practices, including automated testing, CI/CD, documentation, maintainability, and reusable design
• Prioritize ruthlessly, balancing quick tactical fixes with longer-term, scalable solutions

What We're Looking For

• 8+ years of experience building software, data platforms, or analytics platforms
• 3+ years leading engineering teams, including experience managing managers, technical leads, or multiple delivery streams
• A degree in Computer Science or a related field, with a strong academic record
• A track record of setting technical strategy, building roadmaps, aligning stakeholders, and leading teams through ambiguity
• Deep proficiency in Python and SQL, with strong fundamentals in data modeling, data warehousing, distributed compute, and APIs
• Hands-on experience with dbt, cloud data warehouses (Snowflake or Databricks), and orchestration tools such as Dagster
• Solid experience working in a cloud environment (AWS, Azure, or GCP)
• Strong problem-solving judgment and the ability to communicate and influence across both technical and non-technical audiences
• A genuine partnership mindset with data scientists, and the ability to translate analytical needs into robust technical solutions
• Strong people leadership skills, including hiring, coaching, performance management, and building a high-accountability engineering culture
• Experience applying large language models or AI-assisted development tools to improve engineering or analytical workflows

Nice to Have

• Experience with time series analysis, backtesting, statistical methods, or reproducible analytical workflows
• Prior experience in investment management or financial services

Why Join?

This is a chance to work at the intersection of engineering and investing, building tools that feed directly into how a leading fundamental investment team evaluates data and makes decisions in near real time. The firm runs lean by design, so engineers here own full systems end-to-end rather than a narrow slice of a much larger organization, and strong performers are recognized for impact and judgment rather than tenure alone.

As a player-coach, you'll lead a team while staying close to the technical craft, shaping both the people and the platform. The tech stack is modern and built for scale, including Python, SQL, Snowflake or Databricks, Spark, dbt, Dagster, and cloud-native tooling, and there's real appetite to bring in AI-assisted development, balanced with thoughtful judgment about where it genuinely adds value. Backed by a multi-decade track record and tens of billions in committed capital, this is a stable platform with real runway to help shape technical strategy going forward.