Forward Deployed - Data Architect

About the role

The Opportunity

ShyftLabs is a fast-growing data and technology company partnering with Fortune 500 organizations to deliver innovative, data-driven solutions. We specialize in building scalable, enterprise-grade platforms that help businesses unlock the value of their data through modern cloud technologies, advanced analytics, and AI-driven capabilities.

We are seeking an experienced Data Architect to work directly with enterprise clients, providing architectural leadership and hands-on technical expertise across modern data engineering initiatives. This is a client-facing role that combines solution architecture, technical consulting, and delivery oversight, requiring the ability to translate complex business requirements into scalable, secure, and reliable data solutions.

You will collaborate closely with client stakeholders, engineering teams, and technology partners to define architectural direction, establish engineering standards, and guide data platform implementations from design through production. You will also review partner-developed architectures, code, data models, and pipelines to ensure solutions meet enterprise requirements and align with established best practices.

The ideal candidate is a technically strong, hands-on architect who can confidently engage with senior client stakeholders, communicate complex technical concepts clearly, and influence architectural decisions while supporting distributed engineering teams.

What You'll Be Doing -

Lead Client-Facing Architecture Discussions: Work directly with client stakeholders to understand business objectives, technical requirements, existing data environments, and architectural challenges. Translate these needs into practical, scalable data architecture solutions.

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Define Enterprise Data Architecture: Design and recommend modern data platform architectures using Azure Databricks, Delta Lake, and cloud-based data engineering technologies, ensuring alignment with client objectives and enterprise standards.

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Drive Technical Discovery and Solution Design: Facilitate technical workshops, discovery sessions, and architecture reviews to assess current-state environments, identify gaps, evaluate solution options, and define target-state architectures.

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Provide Hands-On Technical Leadership: Develop, validate, and review data engineering solutions using SQL, Python/PySpark, Apache Airflow, and dbt Core, balancing architectural oversight with practical implementation expertise.

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Guide Data Pipeline Development: Establish architecture and implementation standards for ingestion pipelines that integrate APIs, databases, files, and cloud data sources using Databricks-native capabilities such as Lakeflow Connect and Auto Loader.

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Establish Data Architecture Standards: Define and promote reusable architectural patterns, implementation guidelines, and engineering best practices across client projects and distributed delivery teams.