Senior Manager Data Architecture

Circle K•Charlotte, NC

2d•Onsite

About The Position

Circle K is seeking a Sr. Manager, Data Architecture to lead the team responsible for defining how enterprise data is discovered, modeled, integrated, governed, and delivered through its modern global data platform. This high-impact leadership role combines people leadership with deep data architecture expertise. The role sets architectural direction and creates reusable patterns, practical standards, and trusted data products across Azure, Snowflake, Databricks, and related technologies. The position owns architecture across the full data lifecycle, from source discovery and ingestion through curated data products and semantic models. It leads data architects and modelers and partners with engineering, analytics, governance, platform, and business teams to deliver scalable, reusable, business-ready data solutions. The role is based at an established Circle K office and requires five days in the office.

Requirements

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.
Strong transformation leadership and ability to lead teams through ambiguity, modernization, operating model change, and legacy platform migration.
Excellent executive and technical communication skills, including the ability to explain tradeoffs, risks, decisions, and recommendations clearly.

Nice To Haves

Experience in retail, convenience, fuel, merchandising, loyalty, supply chain, finance, store operations, or customer/digital data domains.
Erwin experience.

Responsibilities

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.
Drive decisions across competing priorities and help teams balance speed, quality, reuse, governance, and business value.
Lead the transition from legacy, project-based delivery toward reusable, domain-aligned, product-oriented data platform practices.
Build alignment with data engineering, BI, analytics, platform, governance, product, business leaders, and senior stakeholders.
Guide source discovery and data assessment, including source identification, 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 for consistent business metrics, entities, hierarchies, and self-service analytics.
Define reusable architecture patterns for ingestion, transformation, modeling, data products, semantic models, metadata, lineage, and quality controls.
Establish design review practices that improve quality and consistency without slowing delivery.
Partner with Data Governance to ensure standards support data quality, ownership, stewardship, privacy, security, retention, and regulatory requirements.
Create architecture documentation for engineers, product teams, governance partners, business stakeholders, and senior leaders.
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 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.