Senior Analyst

The Carlyle Group Employee Co.•Washington, DC

4d•Hybrid

About The Position

The Forward Deployed Product Engineer, Global Private Equity Technology is a hands-on engineering role responsible for building the business-critical applications and applied-AI capabilities that power Carlyle’s Global Private Equity (GPE) platform. The Senior Analyst works closely with stakeholders across the private equity business — including Deal Teams, Fund Management, and the broader GPE organization — to build solutions that deliver automation wins and drive measurable value in how deals are sourced, diligenced, executed, monitored, and grown. This is a builder’s role. The Senior Analyst owns defined features and components end-to-end — across backend services, modern frontends, data pipelines, and LLM-based workflows — working within the technical direction and engineering standards set by senior members of the GPE Product and Engineering organization. The ideal candidate pairs strong technical execution with developing product judgment, is comfortable working directly with business users, and is energized by turning Carlyle’s proprietary data and domain expertise into workflow-native capabilities. Unlike a traditional application engineer, the Senior Analyst is measured by business outcomes rather than tickets closed: understanding the problem behind the request, prototyping quickly against real data, hardening what works into production, and supporting adoption with the investment professionals who use it.

Requirements

Bachelor’s degree, required.
Minimum of 2 years of overall relevant experience, required.
Experience in software engineering, required.
Hands-on experience building applications using Python, required.

Nice To Haves

Concentration in computer science, engineering, or a related quantitative field, preferred.
Experience working with relational databases and SQL; exposure to enterprise data platforms (e.g., Snowflake, Databricks, PostgreSQL), preferred.
Frontend development experience with React and/or Next.js, preferred.
Experience building within an agentic harness — multi-step, tool-using agents with defined scaffolding, orchestration, evaluation, and guardrails — preferred.
Experience with document intelligence, including extracting structured data from unstructured source documents (e.g., OCR, layout parsing, and LLM-based extraction from PDFs, agreements, and financial reporting), preferred.
Experience working directly with business users to gather requirements and deliver working software, preferred.
Prior experience in alternative asset management, private equity, financial services, or startup environment preferred.

Responsibilities

Build and maintain features and components of business-critical GPE applications — from design through implementation, deployment, and ongoing support.
Develop full-stack functionality using Python on the backend and React / Next.js on the frontend.
Build APIs, services, and data pipelines that connect GPE’s enterprise data platform (Snowflake), third-party market and portfolio data (e.g., Chronograph, FactSet, PitchBook), and internal systems.
Prototype quickly against real data to validate use cases, then work with senior engineers to harden what works into secure, production-grade software.
Meet the team’s standards for code quality, performance, security, and maintainability.
Work closely with stakeholders across the private equity business — Deal Teams, Fund Management, and the broader GPE organization — to learn their workflows and identify automation opportunities worth building.
Translate business requests into clear technical requirements and shippable deliverables, with guidance from senior engineers and product management on scope and sequencing.
Support adoption through demos, hands-on user enablement, clear documentation, and responsive follow-up on user feedback.
Track usage and business impact of delivered solutions, and share what the data shows with the team and its stakeholders.
Build AI-enabled functionality — LLM-assisted workflows, intelligent automation, and agentic components — where it delivers clear, measurable value across the investment lifecycle.
Implement retrieval-augmented generation (RAG) pipelines, Knowledge Graph implementations, and broader LLM-based workflows using modern orchestration frameworks (and enterprise LLM platforms (e.g., AWS Bedrock, Anthropic Claude, OpenAI Codex).
Apply established patterns for connecting LLMs to proprietary data (e.g., Snowflake accessed via MCP or equivalent) so that solutions remain permissioned, governed, and production-ready.
Partner with data, platform, and engineering teams to move applied-AI capabilities from proof-of-concept into production.
Apply the architectural patterns, reusable components, and engineering standards set by the GPE Product and Engineering organization.
Participate actively in code review, giving and receiving feedback that raises the quality of what the team ships.
Collaborate and communicate with the team to complete shared deliverables.
Contribute clear technical documentation so that work is transparent, supportable, and reusable across Carlyle Technology.
Ensure all solutions comply with Carlyle’s AI governance, data privacy, and information security standards.
Communicate delivery status, blockers, and technical trade-offs clearly to the team and to business stakeholders.