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Open nowFirst seen 6 days ago

Cloud Data Architect Location: 100% Remote

ESRhealthcare and EXEC STAFF RECRUITERS1,405 open roles

Where
Remote, OR, USA
Work mode
Remote
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Your applicationOpen nowCloud Data Architect Location: 100% RemoteESRhealthcare and EXEC STAFF RECRUITERS · Remote, OR, USA

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on ESRhealthcare and EXEC STAFF RECRUITERS's own form.

The reply lands in your private mailbox

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.7%14 days
  5. 35.6%30 days
  6. 58.0%60 days
This job: first seen 6 days ago

The posting

Cloud Data Architect

Location: 100% Remote

Experience Requirements

Bachelor's degree in Computer Science, Engineering, or related field and 10+ years of experience in enterprise data engineering, data warehouse architecture, or cloud data architecture, with demonstrated progression into architectural ownership and technical leadership roles.

Proven experience designing and owning end‑to‑end data architectures, including on‑prem, cloud, and hybrid environments — not simply implementing architectures defined by others.

Deep understanding of hybrid data ecosystems, including:

Coexistence of on‑prem and cloud data platforms

Strategies for moving, synchronizing, and governing data across environments

Designing architectures that support gradual migration rather than lift‑and‑shift

Strong data warehouse and lake house architecture expertise, including:

Designing and operating Bronze / Silver / Gold data layers

Blending and federating data across multiple warehouses (on‑prem and cloud)

Supporting governed analytics, semantic layers, and enterprise reporting

Advanced data modeling skills, with hands‑on experience designing:

3NF and normalized operational models

Dimensional/star schemas

Enterprise semantic and consumption layers for analytics and BI

Expert‑level SQL proficiency across both cloud and traditional RDBMS platforms, with experience in systems such as:

Netezza or other MPP databases

SQL Server and similar enterprise relational platforms

Modern cloud data warehouses or lake house query engines

Cloud platform experience (Azure, AWS, or GCP) with a strong understanding of:

Cloud storage, compute, networking, and security concepts

Cost‑aware architecture design, scaling strategies, and performance optimization

Designing platforms that are resilient, secure, and future‑ready

Experience establishing data governance and architectural standards, including:

Defining data ownership, lineage, quality, and access patterns

Partnering with analytics, security, and business stakeholders to enforce governance at scale

Ability to operate as a senior technical leader, including:

Making architectural trade‑off decisions and clearly articulating rationale

Guiding engineering teams while remaining hands‑on at the design level

Translating business, analytics, and SAP requirements into durable platform designs

Preferred / Bonus Experience

Experience modernizing or migrating legacy on‑prem data warehouses (e.g., Netezza or other MPP systems) to cloud‑native lake house or warehouse architectures.

Hands‑on experience with Databricks (Delta Lake, Spark/PySpark, SQL) and/or Snowflake, including:

Lakehouse or warehouse design patterns

Performance tuning and cost optimization

Security, data sharing, and governance controls

Familiarity with SAP‑centric analytics ecosystems, including SAP Business Data Cloud / Datasphere, SAP Analytics Cloud (SAC) or Integration patterns with SAP S/4HANA and non‑SAP data sources

Experience supporting or modernizing enterprise BI platforms, including migrations from tools such as Cognos to Tableau or Power BI, with a focus on architecture and semantic design (not report development).

Knowledge of data governance frameworks and tooling, such as: Azure Purview, Unity Catalog, or comparable platforms

Exposure to AI/ML enablement architectures

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