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 building a trusted, scalable, secure, governed, and reusable data foundation for enterprise analytics, business intelligence, machine learning, generative AI, and agentic AI. The position combines enterprise architecture ownership with hands-on technical expertise to deliver scalable, secure, resilient, and cost-effective data 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, and provide oversight for analytical, operational, API-based, event-driven, and real-time data workloads. The role includes establishing AI-ready data and responsible AI architecture, setting secure-by-design and operational standards, guiding engineering teams, and collaborating with business and technology stakeholders. It also entails technical leadership in architecture reviews, implementation, incident response, and modernization, with the goal of improving data quality, lineage, discoverability, reuse, reliability, performance, and cost efficiency.

Requirements

12+ years of progressive technology experience, with 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, with hands-on expertise in Redshift, RDS, AWS Glue, Lambda, Kinesis, EventBridge, API Gateway, SQS, SNS, IAM, and CloudWatch.
Experience with data modeling, including designing scalable data structures for analytical and operational workloads.
Experience leading architecture and technical design reviews for complex enterprise solutions.
Ability to develop 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.
Experience with infrastructure as code using AWS CDK or Terraform, CI/CD pipelines, Git, and modern DevOps practices.
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.
Strong expertise in Python and AWS data engineering patterns.
Self-driven approach to staying current with emerging technologies, industry standards, and architectural best practices through continuous research and professional development.

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.

Responsibilities

Define and own enterprise data architecture strategies, future-state architectures, architectural principles, technology standards, and modernization roadmaps aligned with business and enterprise objectives.
Lead architecture assessments, technical feasibility studies, design reviews, and Architectural Review Board activities; recommend technology selections, solution designs, and approaches to architectural risks and exceptions.
Evaluate emerging technologies and architectural patterns, ensuring implementation decisions support long-term enterprise goals, and communicate recommendations and 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, and agentic AI, including 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 data architectures for 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 architecture 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 modern data architecture patterns, including data warehouses, lakehouse architectures, Data Mesh, and domain-oriented data products, to support 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; establish reusable reference architectures, standards, and implementation patterns.
Improve data quality, lineage, discoverability, security, and reuse; reduce architectural duplication and proliferation of point solutions.
Establish the data foundation required to scale analytics, ML, generative AI, and agentic AI, and drive measurable improvements in reliability, performance, cost efficiency, and adoption of architecture standards.