Data Architect, Corporate Services

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, which range from public and private pension funds to wealthy individuals and families to 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." Reflecting this view, 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, [... source excerpt omitted ...] ming 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, and set the standards the team builds to. • 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 [... source excerpt omitted ...] legacy SSIS, SSRS, and SSAS reporting to the modern stack, with reconciliation validation confirming output equivalence before decommission. • 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 laye