Forward Deployed - Data Architect

ShyftLabs; Toronto; On-site; CAD 160,000 - 190,000; Full time

Benefits offered by this job

Fortune 500 clients

Job summary

ShyftLabs is seeking a Data Architect to guide enterprise data platform initiatives with a hands-on, client-facing approach. You will shape architectural direction, drive data governance, and ensure robust, scalable data pipelines using Azure Databricks, Delta Lake, and dbt Core.

You will collaborate with stakeholders, engineering teams, and partners to translate complex business requirements into reliable data solutions, supporting distributed delivery with governance, security, and cost

Qualifications

• 8+ years of experience in data engineering, data architecture, or related roles.
• Proven client-facing technical experience with enterprise customers.
• Strong expertise in Azure Databricks and Delta Lake.
• Deep knowledge of Unity Catalog, data governance, and access controls.
• Proficiency in SQL and Python/PySpark for data processing.
• Hands-on experience with Airflow and dbt Core.
• Familiarity with data ingestion technologies and CI/CD practices.

Responsibilities

• Lead client-facing architecture discussions translating business needs into scalable data solutions.
• Define enterprise data architectures using modern platforms and cloud technologies.
• Drive technical discovery, workshops, and target-state architecture design.
• Provide hands-on leadership across data pipelines, transformations, and modeling.
• Establish data governance, quality frameworks, and reusable architectural patterns.
• Review deliverables, ensuring security, performance, and maintainability.

Skills

Client-facing

Data architecture

Cloud data platforms

SQL/Python

Tools

Azure Databricks

Delta Lake

Unity Catalog

SQL

Python/PySpark

Apache Airflow

dbt Core

Lakeflow Connect

Auto Loader

Job description

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.

• 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.

• 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.

• 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.

• 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.

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

• Implement Medallion Architecture: Design and guide the implementation of Bronze, Silver, and Gold data layers to support reliable ingestion, standardized transformations, curated data products, and downstream analytics.

• Drive Data Governance and Modeling: Establish appropriate practices for Unity Catalog, data access controls, lineage, semantic modeling, data discoverability, and enterprise data governance.

• Oversee Technical Delivery and Quality: Review partner and engineering team deliverables, including architecture designs, code, data models, and pipelines, ensuring alignment with requirements, performance expectations, security standards, and long‑term maintainability.

• Champion Data Quality and Reliability: Define standards for data validation, automated testing, pipeline monitoring, error handling, and operational readiness to ensure reliable production data products.

• Support CI/CD and Engineering Automation: Guide implementation of Git‑based workflows, automated testing, CI/CD pipelines, and controlled deployment practices across development, testing, and production environments.

• Advise on Performance and Cost Optimization: Evaluate platform performance, processing efficiency, scalability, and cloud consumption, recommending improvements that balance technical requirements with business value.

• Drive AI‑Assisted and Spec‑Driven Development: Encourage the effective use of AI‑assisted development tools and specification‑driven engineering practices while maintaining architectural integrity, code quality, and delivery standards.

• Coordinate Distributed Engineering Teams: Provide technical direction, resolve architectural questions, and facilitate collaboration between client stakeholders, internal engineers, and external technology partners.

• Communicate Progress and Technical Risks: Clearly communicate architectural decisions, implementation trade‑offs, dependencies, risks, and recommendations to both technical and non‑technical stakeholders.

• Support End‑to‑End Delivery: Maintain architectural ownership throughout the project lifecycle, from discovery and solution design through implementation, deployment, and ongoing optimization.

What You'll Bring

• 8+ years of overall experience in data engineering, data architecture, or related technical roles, with strong hands‑on experience designing and delivering production‑grade data platforms.

• Proven experience in a client‑facing technical role, such as Data Architect, Solutions Architect, Technical Consultant, or Lead Data Engineer, working directly with enterprise customers or stakeholders.

• Strong hands‑on expertise with Azure Databricks, including the design and implementation of scalable enterprise data solutions.

• Deep understanding of Delta Lake, including data management, processing patterns, and performance optimization.

• Experience with Unity Catalog, including data governance, access controls, lineage, and enterprise data management.

• Strong proficiency in SQL and Python/PySpark for data processing, transformation, and pipeline development.

• Hands‑on experience with Apache Airflow for orchestration and dbt Core for data transformation, testing, and modeling.

• Experience designing ingestion pipelines across APIs, relational databases, files, and other enterprise data sources.

• Familiarity with Databricks‑native ingestion technologies, including Lakeflow Connect and Auto Loader.

• Strong understanding of Medallion Architecture, data products, semantic modeling, and data quality frameworks.

• Experience with CI/CD, Git, automated testing, monitoring, and production data pipeline management.

• Demonstrated ability to review and challenge technical architectures, code, data models, and engineering deliverables while providing constructive, actionable feedback.

• Experience translating business requirements into technical specifications, architectural designs, and implementation roadmaps.

• Ability to evaluate technical trade‑offs and communicate recommendations clearly to technical teams, business stakeholders, and client leadership.

• Experience working with external technology partners, third‑party delivery teams, or distributed engineering organizations.

• Familiarity with AI‑assisted development tools and spec‑driven development practices.

• Strong communication, presentation, stakeholder management, and problem‑solving skills.

• Ability to manage competing priorities, navigate ambiguity, and build trusted relationships with clients while maintaining technical credibility.

Salary Range

• $160,000 - $190,000 (CAD)

Why You’ll Love Working at ShyftLabs

At ShyftLabs, your work matters. We’re a growing data product company making a big impact with Fortune 500 clients and as we scale, you’ll have the chance to shape solutions, influence strategy, and grow your career alongside us.

Inclusion at ShyftLabs

We’re building something big, and we want you on the journey with us.

ShyftLabs is an equal‑opportunity employer committed to creating a safe, diverse, and inclusive environment. We encourage applicants of all backgrounds including ethnicity, religion, disability status, gender identity, sexual orientation, family status, age, and nationality to apply. If you require accommodation during the interview process, let us know and we’ll be happy to support you.