Data Architect, Corporate Services

The Carlyle Group; Washington (District of Columbia); Hybrid; USD 170,000 - 190,000; Full time

Benefits offered by this job

In-office four days per week

Job summary

The Carlyle Group in Washington, DC is expanding its Global Technology & Solutions team with a senior Data Architect for Corporate Services. You will own the CFDW, define data models, govern the semantic layer, and lead a migration from legacy tools to Snowflake, Fivetran, dbt, and Sigma/Power BI.

You will coach engineers and contractors, partner with Finance stakeholders, and ensure high-quality, auditable data across the firm.

Qualifications

• Concentration in computer science, engineering, information systems, or a related quantitative field, preferred.
• Master’s degree, preferred.
• 10+ years of data engineering or data architecture experience, required.
• 7+ years in financial services such as private equity, asset management, investment banking, hedge funds, or fund administration, preferred.
• Demonstrated people management or tech lead experience for a team shipping production data solutions, required.
• Strong hands-on experience with modern cloud data platforms, with Snowflake preferred and Databricks or comparable platforms relevant.
• Hands-on experience with dbt for modular, testable, well-documented transformation models.
• Experience with ingestion and orchestration tooling such as Fivetran, Airflow, or equivalent.
• Experience building governed BI and semantic layers in Sigma Computing or Power BI.
• Track record migrating from legacy SSIS, SSRS, SSAS, or comparable stacks to a modern cloud data platform, including metric translation and reconciliation.
• Strong SQL and dimensional data-modeling skills for analytical and financial datasets.
• Python experience for pipeline tooling, automation, and integration.
• Experience with Azure and/or AWS cloud-native data architecture.
• Experience integrating enterprise systems via APIs, file-based transfers, or event-driven workflows.
• Experience implementing data quality, lineage, governance, and audit controls.
• Exposure to applied AI or LLM enabled workflows in a production setting, preferred.
• Track record of contributing to technical strategy and architectural direction, not only delivery on assigned tasks.

Responsibilities

• Own end-to-end CFDW architecture across ingestion, transformation, semantic layer, and consumption, setting standards the team follows.
• Design and build the Snowflake data platform across landing, integration, and presentation layers, optimized for large-scale financial datasets.
• Build and govern the dbt transformation and semantic layer, defining certified canonical finance metrics as the single source of truth consumed across Sigma and Power BI.
• Own ingestion through Fivetran and other tools as appropriate, including change data capture and schema drift handling across finance source systems.
• Lead the migration of legacy SSIS, SSRS, and SSAS reporting to the modern stack, reconciling and validating output equivalence before decommissioning.
• Stay hands-on as a builder, writing production-grade models and prototypes for the hardest problems while directing the team to deliver the bulk of the build.
• Evaluate and select the right technologies for each problem, bringing in new platforms and tools where they fit rather than forcing a fixed stack.
• Lead and develop a team of analytics and data engineers, setting direction, allocating work, and owning their growth and performance.
• Direct the Manager of Finance Data along with contractor and offshore execution teams, setting technical standards, reviewing deliverables, and holding delivery accountable.
• Establish engineering standards the platform is built to, including infrastructure, configuration, and pipeline as code (e.g., Terraform), CI/CD, automated testing, and observability, partnering with platform and DevOps functions so the platform is reproducible, version controlled, and reliable in production.
• Provide technical direction in vendor engagements, evaluating proposals and ensuring delivery accountability.
• Serve as the primary data platform partner for Finance leadership, translating reporting needs into platform priorities.
• Communicate proactively on incidents and roadmap, calibrating depth to the audience from engineers to senior sponsors.
• Represent Finance Technology in enterprise data governance forums and partner with Information Security, Legal, and Compliance.

Skills

SQL

Python

Data modeling

Leadership

Education

Master’s degree, preferred

Tools

Snowflake

dbt

Fivetran

Power BI

Job description

Company Profile

The Carlyle Group (NASDAQ: CG) is a global investment firm with $475 billion of assets under management, across 678 investment vehicles as of March 31, 2026. Founded in 1987 in Washington, DC, Carlyle has grown into one of the world’s largest and most successful investment firms, with more than 2,500 professionals operating in 28 offices in North America, Europe, the Middle East, Asia and Australia.

Carlyle’s purpose is to connect people, ideas, and capital to fuel growth for companies and performance for investors, ranging from public and private pension funds to wealthy individuals, families, sovereign wealth funds, unions and corporations. Carlyle invests across three segments – Global Private Equity, Global Credit and Carlyle AlpInvest – and has deep expertise across industries, markets, and geographies.

At Carlyle, we believe that a wide spectrum of experiences and viewpoints drives performance and success. Our CEO, Harvey Schwartz, has stated that “To build better businesses and create value for all of our stakeholders, we are focused on assembling leadership teams with the strongest insights from a range of perspectives.” Emphasis is placed on development, retention and inclusion through our internal processes and seven Employee Resource Groups (ERGs). We cultivate a culture where ideas are openly shared and challenged, connecting diverse expertise and perspectives to drive enduring value.

Department Description

The Data Architect, Corporate Services is a senior technical role within Carlyle’s Global Technology & Solutions organization, with ownership over the data architecture underpinning Carlyle’s financial operations. The team is building the Corporate Finance Data Warehouse (CFDW), consolidating fragmented legacy reporting into a single, certified source of finance data subscribed to by teams across the firm. This greenfield opportunity defines the data models, governance standards, and architectural patterns for a platform built to scale with Carlyle’s growth.

Position Summary

This role carries end‑to‑end ownership of the CFDW: the architecture, the build, and the platform that Finance teams, reporting systems, and downstream applications trust for certified data. The architect is accountable for lineage, quality, and reliability of every metric the platform publishes.

The technical challenge is significant: Carlyle’s finance reporting runs on a legacy Microsoft SSIS, SSRS, and SSAS estate, and this role leads its migration to a modern stack anchored in Snowflake, Fivetran, dbt, and Sigma Computing or Power BI. Successful candidates evaluate tools on merit and are comfortable building on any platform that fits the problem.

Equally important is leadership: the role is not an individual contributor. The successful candidate’s primary leverage is through platform architecture, developing engineers, and directing a team of employees and contractors. They build and prototype directly when it matters most, and partner with the Finance Technology Lead and Finance stakeholders to turn reporting needs into durable data products.

What Success Looks Like

Twelve months in, the successful candidate will have moved Carlyle’s corporate finance reporting from a fragmented legacy estate to a working, certified data platform that Finance teams actively subscribe to in place of independent extracts from source systems. The CFDW will be live, covering its initial domains, with legacy SSIS, SSRS, and SSAS reporting retirement underway.

Success is measured by whether Finance trusts it: certified metrics governed through a single semantic layer, end‑to‑end lineage that holds up under audit and SOX scrutiny, and AI‑driven data quality monitoring that surfaces issues before Finance users encounter them. The strongest signal of success is that Finance teams have stopped building around the platform and started building on top of it.

Equally important is the foundation the candidate has built for the long term: a high‑performing engineering team, deep relationships with Finance stakeholders, and an architectural vision that scales well beyond year one.

In‑office requirement: 4 days per week

Primary Responsibilities

Data Platform Architecture & Delivery (≈45%)

• Own the end‑to‑end CFDW architecture across ingestion, transformation, semantic layer, and consumption, setting standards the team follows.
• Design and build the Snowflake data platform across landing, integration, and presentation layers, optimized for large‑scale financial datasets.
• Build and govern the dbt transformation and semantic layer, defining certified canonical finance metrics as the single source of truth consumed across Sigma and Power BI.
• Own ingestion through Fivetran and other tools as appropriate, including change data capture and schema drift handling across finance source systems.
• Lead the migration of legacy SSIS, SSRS, and SSAS reporting to the modern stack, reconciling and validating output equivalence before decommissioning.
• Stay hands‑on as a builder, writing production‑grade models and prototypes for the hardest problems while directing the team to deliver the bulk of the build.
• Evaluate and select the right technologies for each problem, bringing in new platforms and tools where they fit rather than forcing a fixed stack.

Data Governance, Quality & Intelligence (≈25%)

• Build and govern a living data dictionary and end‑to‑end, column‑level lineage that supports audit and SOX control evidence.
• Design a finance knowledge graph and business context layer that capture how finance entities and metrics relate across source systems.
• Implement AI‑driven data quality monitoring that detects anomalies and cross‑system disconnects before they reach Finance users.
• Establish data trust signals and an incident log so Finance users can see data reliability alongside the numbers they consume.
• Apply access controls, classification, and masking to enforce fund‑level and entity‑level security across all consumers.

People Management & Technical Leadership (≈20%)

• Lead and develop a team of analytics and data engineers, setting direction, allocating work, and owning their growth and performance.
• Direct the Manager of Finance Data along with contractor and offshore execution teams, setting technical standards, reviewing deliverables, and holding delivery accountable.
• Establish engineering standards the platform is built to, including infrastructure, configuration, and pipeline as code (e.g., Terraform), CI/CD, automated testing, and observability, partnering with platform and DevOps functions so the platform is reproducible, version controlled, and reliable in production.
• Provide technical direction in vendor engagements, evaluating proposals and ensuring delivery accountability.

Stakeholder Management & Enterprise Alignment (≈10%)

• Serve as the primary data platform partner for Finance leadership, translating reporting needs into platform priorities.
• Communicate proactively on incidents and roadmap, calibrating depth to the audience from engineers to senior sponsors.
• Represent Finance Technology in enterprise data governance forums and partner with Information Security, Legal, and Compliance.

Qualifications

Education & Certifications

• Concentration in computer science, engineering, information systems, or a related quantitative field, preferred.
• Master’s degree, preferred.

Professional Experience

• 10+ years of overall relevant data engineering or data architecture experience, required.
• 7+ years in financial services such as private equity, asset management, investment banking, hedge fund, or fund administration, preferred.
• Demonstrated people management or tech lead experience for a team shipping production data solutions, required.
• Strong hands‑on experience with modern cloud data platforms, with Snowflake preferred and Databricks or comparable platforms relevant.
• Hands‑on experience with dbt for modular, testable, well‑documented transformation models.
• Experience with ingestion and orchestration tooling such as Fivetran, Airflow, or equivalent.
• Experience building governed BI and semantic layers in Sigma Computing or Power BI.
• Track record migrating from legacy SSIS, SSRS, SSAS, or comparable stacks to a modern cloud data platform, including metric translation and reconciliation.
• Strong SQL and dimensional data‑modeling skills for analytical and financial datasets.
• Python experience for pipeline tooling, automation, and integration.
• Experience with Azure and/or AWS cloud‑native data architecture.
• Experience integrating enterprise systems via APIs, file‑based transfers, or event‑driven workflows.
• Experience implementing data quality, lineage, governance, and audit controls.
• Exposure to applied AI or LLM enabled workflows in a production setting, preferred.
• Track record of contributing to technical strategy and architectural direction, not only delivery on assigned tasks.

Competencies & Attributes

• True architect’s mindset: selects the right technology for each problem and is willing to learn and adopt new platforms rather than defaulting to a fixed stack.
• Data trust mindset: treats every published metric as a commitment to its consumers, building certification, lineage, and quality from the start.
• Enterprise understanding: articulates the path forward in handling the complexity and scale of a financial firm’s data and reporting obligations.
• Stakeholder fluency: communicates clearly with both technical and non‑technical audiences and earns the trust of senior Finance leaders.
• Engineering leadership: sets technical direction by example and raises the bar through code review and mentorship.
• Builder’s instinct under ambiguity: turns a vague reporting requirement into a working, certified data product and measures progress in shipped pipelines.

Benefits / Compensation

The compensation range for this role is specific to Washington, DC. The anticipated base salary range is $170,000 to $190,000. In addition to the base salary, the hired professional will enjoy a comprehensive benefits package spanning retirement benefits, health insurance, life insurance and disability, paid time off, paid holidays, family planning benefits and various wellness programs. Additionally, the hired professional may also be eligible to participate in an annual discretionary incentive program, the award of which will be dependent on various factors, including, without limitation, individual and organizational performance.