At CI&T, we help large enterprises transform the potential of AI into real business impact with AI Deployment, AI-native execution, and tech-integrated business solutions.
With 30 years of experience in technological transformation, we accelerate innovation with expertise in Agentic SDLC, Application modernization, Data & AI, Martech and Business strategy.
We are 8,000 CI&Ters across more than 25 countries, collaborating to build solutions with real impact. AI is already part of how we work, evolve, and innovate every day.
At CI&T, our rapid growth is fueled by the innovative solutions we create for our global clients. We are seeking a hands-on Principal Data Architect to own the target architecture for a large-scale data platform modernization at a major US mortgage lender — migrating Finance reporting off a legacy on-premises source system onto a governed cloud data platform (Azure, Microsoft Fabric, and Snowflake), with CDC-based replication and a certified BI semantic layer. This is deliberately a hands-on role: you build alongside the development team through design and build, not a review-from-a-distance seat.
This position calls for a hybrid architect: someone who can design and defend complex technical decisions in front of the client's data and cloud teams at a strategic level, while also working hands-on with the development team to validate and build the solution. A strong grounding in software and data development is essential — profiles limited to analytics work alone will not have the depth this role requires.
Own the end-to-end target architecture spanning CDC replication, lakehouse landing (Fabric/OneLake), and a layered cloud data warehouse (raw/staging → conformed → curated) on Snowflake.
Author and maintain architecture decision records covering transformation mechanism, CDC/load strategy, semantic-model storage mode, warehouse isolation, and cross-platform (lakehouse-to-warehouse) interoperability.
Design the curated data layer and dimensional model, including recomputing source-system runtime-calculated values that don't exist in raw replicated data, and conforming key-value dimension structures into standard reporting attributes.
Design the strategy for a single certified BI semantic model consumed by all downstream reporting tools, with metric definitions versioned in one governed place.
Work hands-on inside the codebase alongside the development team through the design-and-build core.
Lead technical discovery with client architecture, security, and platform teams; resolve open interoperability and network/identity design questions.
Own quality gates for architecture decisions — design validation, baseline lock, cutover readiness — and produce the evidence client stakeholders need to sign off.
Mentor the development team on transformation patterns, incremental materialization, and reconciliation/data-quality design.
Solid experience in data architecture, including hands-on delivery (not review-only) on enterprise data platform modernization programs.
Proven experience architecting cloud data platforms on Snowflake, including layered raw/staging/curated models and incremental transformation patterns (e.g., dynamic or materialized tables).
Experience with Microsoft Fabric/OneLake, or a comparable lakehouse platform, including cross-platform interoperability between a lakehouse and a cloud data warehouse.
Experience architecting change-data-capture (CDC) based replication pipelines (e.g., Qlik Replicate, Fivetran, Debezium, or similar) from a relational source into a cloud platform.
Experience designing governed, certified BI semantic layers (Power BI or equivalent) consumed by multiple reporting tools.
Strong command of data governance fundamentals: classification, masking, row-level access, lineage.
Fluent English (C1) — direct engagement with client architecture, security, and executive stakeholders.
Experience with Microsoft Dynamics 365 Business Central or another ERP as a source system for financial/GL reporting.
Experience in mortgage, lending, or broader financial-services data platforms.
Familiarity with AI-assisted development toolchains inside a governed software development lifecycle (SDLC), where AI output is always a draft requiring human sign-off on production logic.
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