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Lead and perform penetration tests and security assessments across web apps, AD, cloud, network, and infrastructure; run adversary emulation and purple team exercises with SOC/Blue team; assess identity and cloud security; tune SIEM/EDR/Identity Protection detections; prepare technical reports and present findings.

Drive engineering quality by defining testing strategies, building scalable automation frameworks, integrating automated tests into CI/CD, partnering with product owners, coaching engineers on testing practices, and improving observability and release processes to reduce production defects and increase deployment confidence.

Serve as the strategic bridge between business stakeholders and data/AI teams to identify, prioritize, and deliver AI, analytics, and automation solutions. Translate business needs into use cases and requirements, manage delivery backlogs, facilitate Agile ceremonies, define KPIs, and ensure solution adoption and data readiness.

Own the enterprise data platform backlog and roadmap; translate consumer needs into measurable engineering stories; run two-week sprint cadence; author gate criteria for services (SLOs, API maturity, SDK readiness, governance); negotiate service contracts with consumers; track platform KPIs and report to leadership; coordinate cross-team dependencies and manage platform risk register.

Build, operate, and automate enterprise data-governance tooling: PDPA compliance scanners, data-catalog automation, access-control/RLS sync, schema-governance CI, and AI model-governance evidence. Partner with data stewards, legal, security, and platform engineering to operationalize policies, maintain audit evidence, and reduce manual access-ticketing through self-service automation.

Lead pipeline standardization and data-quality programs for the enterprise lakehouse: build compliance gates, deploy DQ frameworks, publish quality dashboards, establish source change management and lineage, own migration playbook, reduce data incidents, lead incident response and partner with platform engineering.

Lead end-to-end product strategy for retail digital products, align stakeholders, manage dependencies and partnerships, and own product KPIs. Maintain backlog, translate user needs into stories, facilitate release planning and demos, integrate usability research, and collaborate with CPO, tech, design, and data teams. Oversee data ingestion, storage, and processing considerations and coordinate advanced analytics implementation to drive business value and user satisfaction.

Design, develop, and deploy end-to-end AI/ML and automation solutions for retail. Build scalable data pipelines, feature engineering, predictive models, and GenAI/LLM applications. Implement MLOps, CI/CD, monitoring, and retraining practices. Collaborate with business and technology teams, participate in Agile ceremonies, and mentor junior data scientists while ensuring documentation, maintainability, and scalability.

Lead design and operation of a company-wide MLOps platform: define lifecycle, run model registry, implement drift and performance monitoring, evaluate/deploy feature store, audit/migrate production models, build standardized wrappers over classical ML libraries, integrate governance and CI/CD, mentor data scientists, and lead incident response.

The Solution Architect will lead software architecture design, ensure quality and performance optimization, mentor team members, and communicate with stakeholders to deliver scalable solutions in retail.