Manager, CPE Data Engineering Lead

The Carlyle Group•Washington, DC

2d•$140,000 - $180,000

About The Position

The Carlyle Group seeks a hands-on engineering manager to lead AI and data engineering for Global Private Equity (GPE). The team builds and operates a platform that combines portfolio company, deal, fund, valuation, and performance data from investment, portfolio monitoring, and enterprise systems. It delivers that data to investment teams, valuations, fund finance, investor relations, and leadership through reporting, applications, and AI-enabled tools. The role designs and ships production AI capabilities, including Model Context Protocol (MCP) services, agents, and integrations that allow investment professionals to work with governed portfolio data in natural language. The platform is cloud-based and built on Snowflake, dbt, and AWS. The manager leads contract engineers, writes code most weeks, sets engineering and evaluation standards, and is accountable for the accuracy, security, and reliability of business-critical AI and data services. The position calls for strong software and data engineering expertise, product judgment, and experience turning ambiguous finance workflows into reliable software, preferably in financial services or private markets.

Requirements

Bachelor’s degree required.
At least 7 years of relevant experience in software engineering, data engineering, or technical analytics.
At least 3 years building and operating production software or data solutions used by business stakeholders.
At least 2 years leading engineering delivery, including backlog ownership, setting technical direction and coding standards, and reviewing developers’ or contractors’ work.
Strong proficiency in Python and SQL, plus experience with C#/.NET or TypeScript.
Demonstrated experience shipping production LLM-powered applications, including tool use and agents, retrieval, structured outputs, and prompt and context design.
Experience evaluating AI systems by designing test cases and regression suites, diagnosing failures, tuning based on measured results, and validating model output against source data.
Hands-on experience with a cloud data platform and modern transformation tooling, such as Snowflake, Databricks, or Azure Synapse with dbt or Spark.
Experience with AWS or Azure, CI/CD such as GitHub Actions, and infrastructure as code such as Terraform.
Knowledge of data architecture, including dimensional and semantic modeling, layered architectures, metadata and lineage, and data quality and reconciliation techniques.
Understanding of security for data and AI systems, including OAuth/OIDC authentication, role- or entitlement-based access to sensitive financial data, and auditability.
Hands-on use of AI coding assistants such as Claude Code, Cursor, or GitHub Copilot in a team setting, including code review and guardrails for AI-generated work.
Strong leadership, mentoring, and communication skills, including the ability to turn ambiguous business needs into working software and collaborate effectively with business teams and contractors.

Nice To Haves

A concentration in computer science, engineering, finance, economics, or another quantitative discipline.
Cloud, data platform, or AI certification, such as AWS, Microsoft Azure, SnowPro, or dbt.
Snowflake and dbt experience.
AWS experience with services such as ECS, Lambda, or S3.
Experience with MCP, agent frameworks, or building tools and integrations for AI assistants.
Experience managing production support, including incident management, root cause analysis, and remediation.
Familiarity with private equity, asset management, transaction advisory, or other financial services data, including fund structures, deals, valuations, portfolio monitoring, and performance metrics.
Experience with Power BI or similar reporting tools.

Responsibilities

Design, build, and deploy LLM-powered tools, agents, and integrations for investment, valuation, and fund finance teams to work with governed portfolio data in natural language, including MCP servers and integrations with enterprise AI platforms and assistants; own solutions from discovery through production and adoption.
Define AI evaluation cases, regression suites, and acceptance criteria; measure accuracy, cost, and latency; gate releases on results; and ensure answers trace back to source data, distinguishing deterministic calculations and validation from model judgment.
Build AI security and governance controls into system architecture, including OAuth/OIDC authentication, entitlement-based row-level security, SQL and tool guardrails, untrusted-content and prompt-injection handling, and audit logging; partner with Information Security, Compliance, and Data Governance.
Lead and mentor contract developers, data engineers, and QA; set technical direction; review designs and code; make build-versus-buy recommendations; and remain hands-on while fostering collaboration, growth, and accountability.
Maintain governed, documented data models and metadata, including metric definitions, glossaries, and lineage, to provide a trusted source of truth for reporting, applications, and AI consumers.
Promote code review, automated unit and dbt testing, data reconciliation, AI evaluations, GitHub Actions CI/CD, Terraform infrastructure as code on AWS, and disciplined promotion through development, QA, UAT, and production.
Own production AI service and data-pipeline stability; lead incident response, root cause analysis, and remediation; and establish monitoring, alerting, and runbooks.
Ensure accurate, auditable data for valuations, fund performance metrics such as IRR, MOIC, DPI, and NAV, and investor reporting; support audit and control reviews.
Support migration of remaining legacy data and reporting workloads to the cloud platform, coordinating with vendor delivery teams.
Adopt AI coding assistants and agent-based workflows across the team, with human review, safety guardrails, and shared team knowledge, to improve delivery speed and quality.
Partner with product owners and business users to translate ambiguous workflows into a prioritized backlog; run Agile delivery in Jira; and communicate progress, risks, dependencies, and technical decisions to senior management and business stakeholders.