Senior Manager Data Architecture

Sr. Manager, Data Architecture

Circle K is a global leader in convenience and fuel, operating in 24 countries and part of the Alimentation Couche-Tard family. We are driving a massive digital transformation, leveraging our global scale and data assets to redefine the customer experience and operational efficiency. Join our Global Data Engineering, Architecture and Enablement team and help build the foundation for data-driven decisions across our entire enterprise.

This is a full-time (5 days in office) role based at an established Circle K office location.

The Role

Circle K is looking for a Sr. Manager, Data Architecture to lead the team that defines how enterprise data is discovered, modeled, integrated, governed, and delivered through our modern global data platform. This is a high-impact leadership role for someone who can combine strong people leadership with deep data architecture expertise. The right candidate will bring structure to ambiguity, set clear architectural direction, and help teams move faster by creating reusable patterns, practical standards, and trusted data products across Azure, Snowflake, Databricks, and related data technologies.

Key Responsibilities and Accountabilities

This role owns the architecture function for the full data lifecycle, from source discovery and ingestion through curated data products and semantic models. The person in this role will lead a team of data architects and modelers while partnering with engineering, analytics, governance, platform, and business teams to deliver scalable, reusable, and business-ready data solutions.

1. Strategy & Leadership

• Lead and develop the Data Architecture team, including data architects and modelers responsible for enterprise data design, modeling standards, and architectural direction.
• Set the data architecture vision and roadmap for Circle K's modern data platform, translating strategy into practical standards, reference architectures, and execution priorities.
• Create focus and drive decisions across competing priorities, helping teams balance speed, quality, reuse, governance, and business value.
• Lead through transformation as teams move from legacy, project-based delivery toward reusable, domain-aligned, product-oriented data platform practices.
• Influence across teams and senior stakeholders, building alignment with data engineering, BI, analytics, platform, governance, product, and business leaders.

2. Own End-to-End Data Architecture

• Guide source discovery and data assessment, including source system identification, data availability, freshness, latency, volume, velocity, and initial source-to-target mapping.
• Define ingestion and raw layer architecture, including batch, streaming, CDC, API, and file-based patterns; raw/bronze landing schemas; and incremental load strategies.
• Establish cleansed and standardized data structures, including silver layer schema design, data type standardization, format normalization, and consistent transformation patterns.
• Own refined and dimensional modeling standards, including facts, dimensions, conformed dimensions, slowly changing dimensions, surrogate keys, business keys, grain definition, and referential relationships.
• Design curated data products that integrate data across domains and are reusable, governed, versioned, and aligned to business consumption needs.
• Influence semantic layer patterns that provide consistent business metrics, entities, hierarchies, and self-service analytics experiences.

3. Establish Architecture Standards and Governance

• Define reusable architecture patterns for ingestion, transformation, modeling, data products, semantic models, metadata, lineage, and quality controls.
• Establish practical design review practices that improve quality and consistency without slowing delivery.
• Partner with Data Governance to ensure architecture standards support data quality, ownership, stewardship, privacy, security, retention, and regulatory requirements.
• Create clear architecture documentation that can be used by engineers, product teams, governance partners, business stakeholders, and senior leaders.

4. Partner Across Delivery and Modernization

• Partner with Data Engineering, BI, Analytics, Platform, DevOps/DataOps, Data Governance, and business product teams to shape integrated data solutions from concept through delivery.
• Support platform modernization and migration from legacy data environments to modern Azure, Snowflake, Databricks, lakehouse, warehouse, and data product patterns.
• Stay technically engaged through design reviews, solution shaping, reference architectures, and targeted prototypes while enabling teams to execute independently.
• Integrate architecture work into delivery planning, including prioritization, estimation, PI planning, backlog readiness, and roadmap execution.

What a Strong Candidate Looks Like

• A proven data architecture leader who has managed or led senior architects, modelers, or technical design teams.
• Someone who has built or modernized enterprise data platforms, preferably using Azure, Snowflake, Databricks, data lake, data warehouse, or lakehouse technologies.
• A hands-on architecture leader who understands data modeling, ingestion patterns, dimensional design, semantic layers, governance, and data product design deeply enough to guide others.
• A practical communicator who can simplify complex architecture decisions for engineers, business partners, governance teams, and executives.
• A transformation-minded leader who can bring structure, standards, and direction without creating unnecessary bureaucracy.
• Someone who has experience in retail, convenience, fuel, merchandising, loyalty, supply chain, finance, store operations, or customer/digital data domains.

Required Qualifications

• 10+ years of experience in data, analytics, enterprise architecture, solution architecture, or related technology roles, including 5+ years focused on data architecture.
• People leadership experience leading, managing, or mentoring data architects, data modelers, or senior technical specialists.
• Strong enterprise data architecture background, including architecture standards, design patterns, roadmaps, governance practices, and cross-domain solution design.
• Modern data platform expertise across cloud data platforms such as Azure, Snowflake, Databricks, data lakes, data warehouses, or lakehouse architectures.
• Deep data modeling experience, including conceptual, logical, physical, dimensional, and semantic modeling.
• Strong knowledge of ingestion and integration patterns, including batch, streaming, CDC, APIs, and file-based data movement.
• Experience with governed data products that support analytics, BI, AI/ML, data science, reporting, and operational insight use cases.
• Experience with data modeling, cataloging, or governance tools; Erwin experience is preferred.
• Strong transformation leadership with the ability to lead teams through ambiguity, modernization, operating model change, and legacy platform migration.
• Excellent executive and technical communication skills, with the ability to explain tradeoffs, risks, decisions, and recommendations clearly.

Why Join Circle K?

You will be joining a high-impact team at a pivotal moment in our technological transformation. You will have the autonomy to define the enterprise-level data landscape for a global organization, directly contributing to business performance and customer experience.