Data Architect, Corporate Services

The Carlyle Group•Washington, DC

46d•$170,000 - $190,000•Hybrid

About The Position

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 in the midst of a high-visibility platform build: the Corporate Finance Data Warehouse (CFDW), which consolidates fragmented legacy reporting into a single, certified source of finance data subscribed to by teams across the firm. This is a greenfield opportunity to define the data models, governance standards, and architectural patterns for a platform built to scale with Carlyle's growth. 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. This architect is accountable for the 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. Carlyle takes a best-of-breed approach, so the successful candidate evaluates tools on their merits and is comfortable building on any platform that fits the problem, not just the ones listed here. Equally important is the leadership dimension. This is not an individual contributor role: 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.

Requirements

Bachelor’s degree, required
10+ years of overall relevant data engineering or data architecture experience, required
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
Track record of contributing to technical strategy and architectural direction, not only delivery on assigned tasks
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, and builds certification, lineage, and quality in 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.

Nice To Haves

Concentration in computer science, engineering, information systems, or a related quantitative field, preferred
7+ years in financial services such as private equity, asset management, investment banking, hedge fund, or fund administration, preferred
Exposure to applied AI or LLM enabled workflows in a production setting, preferred

Responsibilities

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 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, 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.
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.
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 the 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.