Senior Data Architect (Data Foundation & AI Data Readiness)
New
P
Phase2Enterprise data
United StatesFull-TimeSenior
SalaryUSD 132000 - 155000 / year
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Job Details
- Experience
- 8+ years of experience in data engineering, data architecture, or a closely related discipline (e.g., analytics engineering, data platform engineering, or MDM architecture)
- Required Skills
- SQLData engineeringData modeling
Requirements
- Have 8+ years of experience in data engineering, data architecture, or a closely related discipline.
- Demonstrate experience architecting and delivering production data platforms and solutions, including integration, transformation, modeling, and storage.
- Apply sound judgment around scalability, performance, reliability, and cost.
- Be fluent in SQL and data modeling, including approaches such as Kimball and Data Vault.
- Demonstrate ability to establish and apply data quality, testing, and validation practices.
- Be able to prototype and build solutions across the data and application stack.
- Trace data problems, such as incorrect reports or unreliable AI answers, to their root causes.
- Navigate complex stakeholder environments and align technical, business, security, and compliance considerations.
- Communicate with executives, tell a clear progress story, and influence roadmaps using defined scorecards and KPIs.
- Deliver visible progress in short cycles, showing clients tangible movement every week or two.
- Be comfortable with ambiguity and owning varied client data challenges.
- Expect travel, sometimes frequent.
Responsibilities
- Lead technical client conversations, diagnose data challenges, and translate business needs into practical solutions.
- Make the business case for foundational data work and communicate measurable outcomes through demos and executive updates.
- Establish reusable data architectures, technical standards, and delivery approaches.
- Define semantic and context-layer standards and resolve gaps affecting data consistency, accessibility, and reliability.
- Design metadata, lineage, and data quality practices that improve discoverability and reduce ambiguity.
- Architect, build, and optimize data platforms and pipelines for ingestion, integration, transformation, modeling, and storage.
- Deliver proofs of concept and production-ready data solutions across client environments.
- Collaborate with software engineers and technical specialists to implement solutions and reusable engineering patterns.
- Establish data quality, validation, testing, documentation, metadata, lineage, and governance practices.
- Review data solutions developed by engineers and AI-assisted tooling for consistency, maintainability, and correctness.
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