Sr. Data & Analytics Architect

Grocery Outlet Career•Emeryville, CA

10d

About The Position

We are seeking a highly experienced Senior Data & Analytics Architect to lead the design and implementation of scalable data platforms and Analytics solutions across the organization. This role is responsible for translating business needs into robust data architectures, enabling advanced analytics, machine learning, and AI capabilities that drive strategic decision-making and operational efficiency. The ideal candidate has deep expertise in data architecture, cloud platforms, AI/ML frameworks, and enterprise integration, along with strong leadership and stakeholder engagement skills. The Sr. Data & Analytics Architect will report to the VP, Data & Development.

Requirements

10+ years of experience in data engineering, analytics, or platform architecture.
5+ years designing cloud-based data platforms and AI/ML solutions.
Strong expertise with data architecture patterns (data lake, lakehouse, warehouse).
Experience with AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn, or similar).
Hands-on experience with big data technologies (Spark, Databricks, Hadoop ecosystem, etc.).
Strong knowledge of SQL, Python, and distributed data systems.
Experience implementing MLOps pipelines and model lifecycle management.
Excellent communication and stakeholder management skills.
Experience with Generative AI and LLM-based applications.
Knowledge of data governance platforms and metadata management.

Nice To Haves

Industry experience in retail, e-commerce, finance, or supply chain.
Relevant certifications (AWS/Azure/GCP Architect, Databricks, Snowflake).
Knowledge of SAP systems and data structures is a big plus in addition to other data platforms.

Responsibilities

Data & AI Architecture: Design and implement enterprise-scale data architectures supporting analytics, machine learning, and AI applications.
Architect modern data platforms including data lakes, lakehouses, warehouses, and real-time pipelines.
Define AI/ML solution architecture including model training, deployment, monitoring, and governance.
Design and support dashboard development using PowerBI, Databricks, etc.
Cloud & Platform Engineering: Lead architecture for data and AI workloads on cloud platforms (AWS, Azure, or GCP).
Implement scalable pipelines using technologies such as Spark, Databricks or similar.
Optimize system performance, scalability, security, and cost.
AI & Machine Learning Solutions: Architect and guide development of AI-driven solutions including predictive analytics, NLP, recommendation systems, and generative AI applications.
Support MLOps and AI lifecycle management including model deployment, monitoring, and versioning.
Enterprise Integration: Design integrations between data platforms, operational systems, and AI services.
Establish APIs, event-driven architectures, and data sharing frameworks.
Governance & Security: Define and implement data governance, security, and compliance standards.
Ensure adherence to privacy regulations and enterprise security frameworks.
Stakeholder Engagement: Collaborate with business leaders, product teams, and engineering teams to translate business problems into technical solutions.
Lead architecture reviews and provide technical guidance across multiple initiatives.
Leadership & Mentorship: Mentor data engineers, ML engineers, and architects.
Establish architecture standards, design patterns, and best practices.
Influence strategic technology decisions across the organization.