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.
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.
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.
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