Manager, CPE Data Engineering Lead

The Carlyle Group; Washington (District of Columbia); Presencial; USD 180.000 - 260.000; Jornada completa

Descripción de la vacante

The Carlyle Group seeks a hands-on Engineering Manager to lead AI and data engineering for Global Private Equity. You will design and ship production AI capabilities on a Snowflake/AWS cloud platform, manage contract engineers, and set engineering standards while delivering reliable data services to investment teams and leadership.

You will oversee AI/LLM tool development, evaluation, security, and governance, with a focus on data quality and scalable data products.

Formación

• 7+ years of software engineering, data engineering or technical analytics experience.
• 3+ years building and operating production software or data solutions used by business stakeholders.
• 2+ years leading engineering delivery, backlog ownership and setting technical direction.
• Strong proficiency in Python and SQL, plus experience with C#/.NET or TypeScript.
• Experience shipping LLM-powered applications to production and using AI tools in a team setting.
• Experience with cloud data platforms and modern transformation tooling (Snowflake, Databricks, dbt, Spark).
• Experience with AWS or Azure, CI/CD and infrastructure as code (Terraform).
• Knowledge of MCP, governance, and data security practices.

Responsabilidades

• Design, build and deploy LLM-powered tools, agents and integrations for governance and natural-language data access.
• Define evaluation cases, regression suites and acceptance criteria for AI features.
• Build security and governance controls into architecture (OAuth/OIDC, row-level security, guardrails).
• Lead and mentor contract engineers, data engineers and QA while coding regularly.
• Maintain governed data models and metadata for trust and single source of truth.
• Champion automated testing, CI/CD and IaC across environments.
• Own production AI services reliability, incident response and runbooks.
• Ensure data quality and auditable data for valuations and investor reporting.
• Support cloud modernization and migration of workloads to the platform.
• Adopt AI coding assistants and agent workflows with guardrails and documentation.
• Collaborate with product owners to translate business needs into backlog and deliverables.

Conocimientos

Python

SQL

C#/.NET

TypeScript

LLM-powered apps

AI/ML

Cloud platforms

Leadership

Security & governance

Data engineering

Educación

CS/Engineering/Finance degree

Cloud or AI certification

Herramientas

Snowflake

dbt

AWS

Azure

Databricks

Terraform

GitHub Actions

Power BI

Jira

Descripción del empleo

The Carlyle Group seeks a hands-on engineering manager to lead AI and data engineering for Global Private Equity (GPE). The team builds and runs the platform that brings together portfolio company, deal, fund, valuation and performance data from Carlyle's 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 a team of contract engineers, writes code most weeks, sets the engineering and evaluation bar, 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 depth with sound product judgment and a track record of turning ambiguous finance workflows into reliable software, preferably in financial services or private markets.

Responsibilities:

• AI Applications & Agents: Design, build and deploy LLM-powered tools, agents and integrations that let investment, valuation and fund finance teams work with governed portfolio data in natural language. This includes MCP servers and integrations with the firm's enterprise AI platforms and assistants. Own each solution from discovery through production and adoption.
• AI Evaluation & Quality: Define evaluation cases, regression suites and acceptance criteria for AI features. Measure accuracy, cost and latency, and gate releases on the results. Keep a clear boundary between deterministic calculation and validation on one side and model judgment on the other, so that answers trace back to source data.
• AI Security & Governance: Build controls into the architecture rather than adding them afterward, including OAuth/OIDC authentication, entitlement-based row-level security, SQL and tool guardrails, handling of untrusted content and prompt injection, and audit logging. Partner with Information Security, Compliance and Data Governance.
• Team Leadership: Lead and mentor a team of contract developers, data engineers and QA. Set technical direction, review designs and code, make build-versus-buy recommendations, and stay hands-on in the codebase while fostering collaboration, growth and accountability.
• Data Products & Semantics: Maintain governed, well-documented data models and metadata (metric definitions, glossaries and lineage) so that reporting, applications and AI consumers share a single, trusted source of truth.
• Engineering Practices: Champion code review and automated testing (unit tests, dbt tests, data reconciliation and AI evaluations), along with CI/CD through GitHub Actions, infrastructure as code with Terraform on AWS, and disciplined promotion across development, QA, UAT and production.
• Production Support & Reliability: Own the stability of production AI services and the data pipelines behind them. Lead incident response, root cause analysis and remediation, and put monitoring, alerting and runbooks in place.
• Data Quality & Controls: Ensure accurate, auditable data for valuations, fund performance metrics (such as IRR, MOIC, DPI and NAV) and investor reporting, and support audit and control reviews.
• Platform Modernization: Support the transition of remaining legacy data and reporting workloads to the cloud platform, coordinating with vendor delivery teams.
• AI-Enabled Delivery: Adopt AI coding assistants and agent-based workflows across the team, with human review, safety guardrails and shared team knowledge (rules, skills and documentation), to improve delivery speed and quality.
• Business Partnership & Communication: Work with product owners and business users to turn ambiguous workflows into a prioritized backlog. Run Agile delivery in Jira, and communicate progress, risks, dependencies and technical decisions clearly to senior management and business stakeholders.

Requirements:

Education & Certificates

• Concentration in computer science, engineering, finance, economics or another quantitative discipline, preferred
• Cloud, data platform or AI certification (for example AWS, Microsoft Azure, SnowPro or dbt), preferred

Professional Experience

• 7+ years of overall relevant experience in software engineering, data engineering or technical analytics, required
• 3+ years building and operating production software or data solutions used by business stakeholders, required
• 2+ years leading engineering delivery, including owning a backlog, setting technical direction and coding standards, and reviewing the work of developers or contractors, required
• Strong proficiency in Python and SQL, plus experience with C#/.NET or TypeScript, required
• Demonstrated experience shipping LLM-powered applications to production users, including tool use and agents, retrieval, structured outputs, and prompt and context design, required
• Experience evaluating AI systems by designing test cases and regression suites, diagnosing failures, tuning against measured results, and validating model output against source data, required
• Hands-on experience with a cloud data platform and modern transformation tooling (for example Snowflake, Databricks or Azure Synapse, with dbt or Spark), required. Snowflake and dbt strongly preferred
• Experience with AWS or Azure, CI/CD (for example GitHub Actions) and infrastructure as code (Terraform), required. AWS experience (ECS, Lambda, S3) is a plus
• Experience with the Model Context Protocol (MCP), agent frameworks, or building tools and integrations for AI assistants, preferred
• Knowledge of data architecture, including dimensional and semantic modeling, layered architectures, metadata and lineage, and data quality and reconciliation techniques, required
• Understanding of security for data and AI systems, including OAuth/OIDC authentication, role- or entitlement-based access to sensitive financial data, and auditability, required
• Experience managing production support, including incident management, root cause analysis and remediation, preferred
• Familiarity with private equity, asset management, transaction advisory or other financial services data, such as fund structures, deals, valuations, portfolio monitoring and performance metrics, strongly preferred
• Experience with Power BI or similar reporting tools, preferred
• Hands-on use of AI coding assistants (for example Claude Code, Cursor or GitHub Copilot) in a team setting, including code review and guardrails for AI-generated work, required
• Strong leadership, mentoring and communication skills, with the ability to turn ambiguous business needs into working software and to work effectively with business teams and contractors