Application Developer II - Private Markets Technology

Neuberger Berman•New York, NY

6d•$90,000 - $110,000•Hybrid

About The Position

Neuberger's technology team is looking for a Developer to join our Private Markets Front Office Technology group. You'll partner directly with investment professionals to build the applications and data capabilities that power portfolio construction and exposure analytics across our private markets strategies. This role sits at the intersection of software engineering and data work. You'll spend the majority of your time building robust, scalable applications, and a meaningful portion designing data models, queries, and pipelines that turn raw portfolio data into decision-ready insight. It's an excellent opportunity for a developer who enjoys working close to the numbers and the business.

Requirements

Bachelor's degree in computer science, software engineering, mathematics, a quantitative discipline, or equivalent practical experience.
2 to 4 years of professional software development experience.
Strong SQL proficiency, including complex queries, stored procedures, views, window functions, joins, and performance tuning.
Strong Python skills for pipeline building, data analysis, and scripting, including experience working with data libraries (e.g., pandas, NumPy).
Experience working with relational databases (SQL Server preferred) and modeling data for reporting and analytics.
Hands-on experience building or maintaining data pipelines that move and transform data across systems, ensuring data integrity.
Solid analytical mindset, comfortable interpreting business requirements, producing accurate data outputs, and identifying data discrepancies.
Experience writing unit, integration, and data tests (e.g., pytest), and comfort with Git-based workflows and code reviews.
Strong communication and collaboration skills; ability to work with both technical and business stakeholders, share ideas and opinions on potential solutions, and voice well-reasoned views even when they differ from the consensus.
Proven ability to quickly learn new technologies and domain concepts.
Proactive, ownership mindset with commitment to clean, maintainable, well-documented code, and a high bar for both your own work and that of the team.

Nice To Haves

Experience in financial services, asset management, or fintech, particularly private markets (private equity, private credit, real assets, fund-of-funds).
Familiarity with portfolio construction concepts or investment performance and exposure analytics.
Experience with a Python web framework (FastAPI preferred; Flask, Django, Dash, or Streamlit a plus).
Experience with React and modern JavaScript/TypeScript for building responsive UIs.
Experience with data visualization tools such as Tableau or Power BI.
Exposure to statistical/analytical modeling or working with large, heterogeneous datasets.
Familiarity with CI/CD pipelines and DevOps tooling (Git, Azure DevOps, Jenkins, or similar).
Experience deploying applications to Kubernetes/OpenShift; exposure to Azure, AWS, or GCP.
Experience in an Agile/Scrum environment.

Responsibilities

Design, build, and maintain SQL stored procedures, views, and queries to support reporting for investment professionals and internal stakeholders.
Investigate and debug data issues, including query logic, query performance, and pipeline failures, tracing discrepancies back to their root cause.
Develop and maintain Python applications that ingest files, transform them into standardized formats, and load them into the data warehouse.
Analyze and validate data to fulfill internal stakeholder requests, ensuring accuracy and completeness.
Build and optimize the data layer behind portfolio construction, allocation, and exposure analytics tools, including SQL schema design and data modeling for portfolio and fund data.
Develop quantitative and analytical features such as portfolio aggregation and performance/exposure metrics, translating investment logic into clean, well-tested code.
Collaborate closely with investment teams to understand workflows, ask the right questions to clarify requirements, identify pain points, and deliver solutions that are clearly actionable.
Implement unit, integration, and data-quality tests; apply best practices in database management, security, and performance optimization.
Participate in agile ceremonies (stand-ups, sprint planning, retrospectives), code reviews, and maintain technical documentation.
Use AI coding tools (e.g., GitHub Copilot, Claude Code) to enhance productivity while maintaining strong independent coding and debugging skills.