Sr. AWS Data Architect

Signet Jewelers•Akron, OH

About The Position

Signet Jewelers is seeking an experienced Sr. AWS Data Architect to provide architectural leadership for the design, modernization, governance, and evolution of enterprise data platforms. The role focuses on establishing a trusted, scalable, secure, governed, and reusable data foundation for enterprise analytics, BI, machine learning, generative AI, and agentic AI. It combines enterprise architecture ownership with hands-on technical expertise to deliver scalable, secure, resilient, and cost-effective solutions aligned with business objectives and enterprise data strategy. The architect will define future-state data architecture, reference architectures, technology standards, design patterns, and strategic roadmaps, while providing oversight across analytical, operational, API-based, event-driven, and real-time data workloads. The role includes establishing an AI-ready data foundation and reusable patterns for RAG, embeddings, vector search, enterprise knowledge integration, and governed model access. The architect will partner with governance, security, engineering, and AI teams to set responsible AI data controls and standards. The position also provides hands-on technical leadership through architecture walkthroughs, code reviews, proof-of-concept development, implementation guidance, and design reviews. The architect will guide engineering teams in building maintainable production solutions with reusable components, infrastructure as code, CI/CD, Git, and modern DevOps practices; lead technical problem resolution and incident reviews; and promote modern data architectures and modeling standards. The role partners with business and technology stakeholders, mentors engineers, and drives measurable improvements in data quality, lineage, discoverability, security, reuse, reliability, performance, cost efficiency, and adoption of architecture standards.

Requirements

12+ years of progressive technology experience, including significant experience in enterprise data architecture, cloud architecture, and data engineering.
Deep understanding of modern data architecture patterns, including data lakes, data warehouses, lakehouse architectures, Data Mesh, data products, and event-driven architectures.
Proven experience designing and operating enterprise-scale data platforms on AWS.
Hands-on expertise with Amazon Redshift, RDS, AWS Glue, Lambda, Kinesis, EventBridge, API Gateway, SQS, SNS, IAM, and CloudWatch.
Solid experience with data modeling and designing scalable data structures for analytical and operational workloads.
Experience leading architecture and technical design reviews for complex enterprise solutions.
Demonstrated experience developing architecture strategies, reference architectures, technical designs, enterprise standards, and technology roadmaps.
Strong understanding of scalability, resiliency, high availability, disaster recovery, performance optimization, cloud security, networking fundamentals, IAM, and cloud cost management.
Demonstrated ability to lead and influence highly technical teams, mentor engineers, build consensus across stakeholders, and communicate architecture recommendations effectively to technical and executive audiences.

Nice To Haves

Experience with Data Mesh, domain-oriented architecture, data products, or data contracts.
Experience with agentic AI or AI-driven data ingestion using LLMs for data product creation and developer enablement.
Experience with infrastructure as code using AWS CDK or Terraform, CI/CD pipelines, Git, and modern DevOps practices.
Self-driven interest in keeping current with emerging technologies, industry standards, and architectural best practices through continuous research and professional development.

Responsibilities

Define and own enterprise data architecture strategies, future-state architectures, architectural principles, technology standards, and modernization roadmaps.
Lead architecture assessments, technical feasibility studies, design reviews, and Architectural Review Board activities; recommend technology selections, solution designs, risk responses, and exceptions.
Evaluate emerging technologies and architectural patterns, aligning tactical implementation decisions with long-term enterprise goals and communicating trade-offs to business and technology leadership.
Define the architecture and roadmap for an AI-ready enterprise data foundation supporting analytics, machine learning, generative AI, agentic AI, and secure consumption of structured, semi-structured, and unstructured data.
Define reusable architecture patterns for RAG, embeddings, vector search, enterprise knowledge integration, and governed model access.
Partner with Data Governance, Security, Engineering, and AI teams to establish responsible AI data controls and standards.
Architect scalable, secure, highly available, resilient, and cost-effective data platforms using AWS native services.
Design end-to-end architectures for data ingestion, integration, storage, processing, transformation, and consumption across batch, streaming, event-driven, API-based, analytical, and operational workloads.
Establish architectural best practices for performance optimization, disaster recovery, cloud security, observability, and cost management.
Define secure-by-design standards covering IAM, least-privilege access, encryption, network connectivity, data protection, and compliance requirements.
Establish standards for monitoring, logging, alerting, fault tolerance, high availability, backup and recovery, disaster recovery, and operational readiness.
Provide hands-on technical leadership through architecture walkthroughs, code reviews, proof-of-concept development, implementation guidance, and design reviews.
Guide engineering teams in developing maintainable, production-ready solutions using reusable components, infrastructure as code, CI/CD, Git, and modern DevOps practices.
Lead technical problem resolution for critical systems, participate in incident response and root cause analysis, and recommend corrective actions to improve reliability, performance, and operational maturity.
Define and promote data warehouse, lakehouse, Data Mesh, and domain-oriented data product patterns for analytics, reporting, and AI/ML use cases.
Lead data modeling across analytical domains, defining standards for data structures, schema design, data contracts, and structured and semi-structured formats such as relational data, JSON, and Parquet.
Partner with business stakeholders, enterprise architects, data engineers, analysts, application teams, and integration partners to translate business requirements into scalable enterprise data solutions.
Mentor engineers and influence cross-functional technical decisions while fostering architecture discipline, collaboration, innovation, and continuous improvement.
Deliver and maintain the future-state AWS data architecture and modernization roadmap, and establish reusable reference architectures, standards, and implementation patterns.
Improve data quality, lineage, discoverability, security, and reuse; reduce architectural duplication and proliferation of point solutions; and establish the foundation to scale analytics, ML, GenAI, and agentic AI.