Manager, CPE Data Engineering Lead

The Carlyle Group Employee Co. • Washington, DC

1d

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 consolidates portfolio company, deal, fund, valuation and performance data from investment, portfolio monitoring and enterprise systems, and delivers it 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 let investment professionals work with governed portfolio data in natural language on a cloud platform 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 the AI and data services the business depends on. A successful candidate combines strong software and data engineering skills with product judgment and experience turning ambiguous finance workflows into reliable software, preferably in financial services or private markets.

Requirements

• Bachelor’s degree required.
• Concentration in computer science, engineering, finance, economics or another quantitative discipline preferred.
• 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, technical direction, coding standards and review of 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 against 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, 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, with the ability to turn ambiguous business needs into working software and work effectively with business teams and contractors.

Nice To Haves

• Cloud, data platform or AI certification, such as AWS, Microsoft Azure, SnowPro or dbt.
• Snowflake and dbt experience.
• AWS services experience, including ECS, Lambda or S3.
• Experience with the Model Context Protocol (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 that enable 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 AI solutions from discovery through production and adoption.
• Define AI evaluation cases, regression suites and acceptance criteria; measure accuracy, cost and latency; and gate releases on evaluation results.
• Maintain a clear boundary between deterministic calculation and validation and model judgment, ensuring answers can be traced to source data.
• 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 in the codebase.
• Maintain governed, well-documented data models and metadata, including metric definitions, glossaries and lineage, so reporting, applications and AI consumers share a trusted source of truth.
• Champion code review and automated testing, including unit tests, dbt tests, data reconciliation and AI evaluations; CI/CD through GitHub Actions; Terraform-based infrastructure as code on AWS; and disciplined promotion across development, QA, UAT and production.
• Own the stability of production AI services and supporting data pipelines; 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.

Benefits

• Comprehensive benefits package spanning retirement benefits, health insurance, life insurance and disability.
• Paid time off and paid holidays.
• Family planning benefits.
• Various wellness programs.
• May be eligible to participate in an annual discretionary incentive program, dependent on individual and organizational performance.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager