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Signet Jewelers is the world's largest retailer of diamond jewelry, operating more than 2,800 stores worldwide under the iconic brands: Kay Jewelers, Zales, Jared, H.Samuel, Ernest Jones, Peoples, Banter by Piercing Pagoda, Rocksbox, JamesAllen.com and Diamonds Direct. We are a people-first company and this core value is at the heart of everything we do, from empowering our valued team members, to collaborating with our customers, to fostering the communities in which we live and serve. People – and the love their actions inspire – are what drive us. We’re not only proud of the love we inspire outside our walls, we’re especially proud of the diversity, inclusion and equity we’re inspiring inside. There are dynamic career paths awaiting you – rewarding opportunities to impact the lives of others and inspire love. Join us!

We are seeking an experienced Sr. AWS Data Architect to provide architectural leadership for the design, modernization, governance and evolution of enterprise data platforms, with a strong focus on establishing a trusted, scalable, secure, governed and reusable data foundation that enables enterprise analytics, BI, machine learning, generative AI and agentic AI.
This role combines enterprise architecture ownership with hands on technical expertise to deliver scalable, secure, resilient and cost effective data solutions aligned with business objectives and the enterprise data strategy.
The ideal candidate will define the future state data architecture, reference architectures, technology standards, design patterns, and strategic roadmaps while providing architectural oversight across analytical, operational, API based, event driven, and real time data workloads.

Major Responsibilities

1. Enterprise Data Architecture and Strategy

• 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, providing recommendations on technology selection, solution design, architectural risks and exceptions.
• Evaluate emerging technologies and architectural patterns, ensuring tactical implementation decisions support long term enterprise goals, and communicate technical recommendations and trade-offs to business and technology leadership.

2. AI Ready Data Foundation & Architecture

• Define the architecture and roadmap for an AI ready enterprise data foundation that supports analytics, ML, GenAI and agentic AI solutions, enabling secure and scalable 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, including Amazon S3, Redshift, RDS, AWS Glue, Lambda, Kinesis, EventBridge, API Gateway, SQS, SNS, IAM, and CloudWatch.
• 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 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 using appropriate AWS services.

 

3. Technical Leadership and Engineering Excellence

• Provide hands on technical leadership through architecture walkthroughs, code reviews, POC development, implementation guidance and design reviews, with strong expertise in Python and AWS data engineering patterns.
• 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 platform reliability, performance and operational maturity.
• Define and promote modern data architecture patterns, including data warehouses, lakehouse architectures, Data Mesh, and domain-oriented data products, supporting 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.

4. Stakeholder Collaboration and Innovation

• 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 and foster architecture discipline, collaboration, innovation, and continuous improvement.

Key Architecture Deliverables & Outcomes

• 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, GenAI and agentic AI.
• Drive measurable improvements in reliability, performance, cost efficiency and architecture-standard adoption.

 

Preferred Skills & Qualifications

• 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.
• Proven experience designing and operating enterprise scale data platforms on AWS.
• Solid 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.
• Experience with Data Mesh, domain-oriented architecture, data products or data contracts is preferred.
• Self-driven to stay current with emerging technologies, industry standards, and architectural best practices through continuous research and professional development
• 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.
• 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.
• 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.