Solution Architect - Martech at Encore Boston Harbor in Las Vegas, NV, United States

Job description

• The Martech Solution Architect is a senior technical role within Wynn's Customer Data Platform (CDP) team, responsible for the design, development, and evolution of Wynn's custom campaign management application and the broader marketing technology ecosystem. This role combines deep data engineering and solution architecture expertise with hands-on product development capability, owning the end-to-end technical design of martech integrations, data pipelines, and application components that power guest engagement and campaign execution across Wynn Resorts!
• The ideal candidate is an AI-native practitioner who actively uses AI tooling to accelerate design, development, and delivery — not as an experiment, but as a standard part of how they build. They bring full-stack awareness, strong data architecture instincts, and the engineering discipline to operate within CI/CD pipelines on GitHub. They are equally comfortable whiteboarding a solution architecture with stakeholders and writing production-quality code or data pipeline logic.

Responsibilities

• Campaign Management Application Development & Ownership
• Lead the technical design and ongoing development of Wynn's custom campaign management application — owning architecture decisions, feature delivery, and platform evolution roadmap.
• Design and implement application components across the full stack — including front-end interfaces, back-end APIs, and data integration layers — with production-quality standards for reliability, scalability, and maintainability.
• Define and enforce the application's data model and integration contracts; ensure campaign execution data flows correctly between the campaign management application, the enterprise data warehouse, and downstream martech activation platforms.
• Identify and remediate technical debt within the campaign management application; propose phased modernization initiatives with clear business impact framing for leadership review.
• Manage the application development lifecycle in GitHub — branching strategy, pull request review standards, release tagging, and deployment pipelines — in alignment with enterprise CI/CD practices.
• Martech Solution Architecture
• Design end-to-end solution architectures for Wynn's martech ecosystem — spanning campaign management, customer engagement platforms, CRM integration, guest feedback systems, and enterprise data warehouse connectivity.
• Produce architecture artifacts — including C4-level component diagrams, integration sequence diagrams, data flow maps, and API design specifications — to a standard suitable for engineering handoff and executive review.
• Evaluate new martech capabilities, platforms, and integration patterns; provide structured build vs. buy vs. integrate recommendations with TCO and risk analysis.
• Define and govern integration standards for martech data flows — including payload schemas, event contracts, error handling patterns, and data latency SLAs — across the enterprise integration layer.
• Partner with the Lead Data Architect and VP to align martech solution design with the broader CDP platform strategy, data governance policies, and enterprise architecture principles.
• Data Engineering & Pipeline Design
• Design and implement data pipelines that move campaign, engagement, and behavioral data between martech source systems, the enterprise data warehouse, and activation platforms — with appropriate transformation, validation, and error-handling logic.
• Define data engineering patterns for martech ingestion — including schema mapping, data quality gate design, CDC (change data capture) patterns, and idempotent load strategies — for adoption by the broader data engineering team.
• Contribute to dimensional and relational data model design for martech subject areas; ensure pipeline outputs conform to agreed grain, key, and SCD strategies documented by the data architecture team.
• Instrument data pipelines with observability — monitoring hooks, alerting thresholds, and DQ validation checkpoints — so failures surface to the CDP team before downstream impact.
• Apply disciplined engineering practices to all pipeline and schema work: read before write, single-purpose changes, documented rollback steps, and peer review before production deployment.
• AI-Driven Product Development
• Actively use AI coding assistants, code generation tools, and LLM-based development workflows to accelerate campaign management application feature delivery and martech solution prototyping — treating AI tooling as a standard engineering accelerant, not an experiment.
• Design and prototype AI-assisted capabilities within the campaign management application and the broader martech platform — including intelligent campaign recommendations, audience segmentation assistance, and natural language interfaces for campaign configuration.
• Define responsible AI integration patterns for martech use cases: prompt design, output validation, hallucination guardrails, and human-in-the-loop review workflows where required.
• Stay current with the AI tooling landscape relevant to martech, campaign management, and data engineering; bring concrete capability proposals to the VP and Lead Data Architect for prioritization.
• CI/CD, DevOps & Engineering Governance
• Own and maintain CI/CD pipeline configuration for the campaign management application and associated martech integration components on GitHub — including automated test gates, linting, build validation, environment promotion, and deployment automation.
• Define and enforce branching conventions, pull request standards, code review practices, and release management processes for the martech engineering workstream.
• Implement automated testing coverage for the campaign management application — unit, integration, and regression test suites — as part of the CI pipeline; set and maintain coverage thresholds for production-bound code.
• Participate in infrastructure-as-code practices for cloud-hosted components of the martech stack; ensure environment configuration is version-controlled and reproducible.
• Champion engineering quality across the CDP team — code review participation, documentation standards, and knowledge transfer for architectural decisions.
• Stakeholder Collaboration & Delivery
• Translate martech business requirements into detailed technical specifications, acceptance criteria, and sprint-ready User Stories within the team's ADO delivery framework.
• Serve as the primary technical point of contact for martech vendor and integration partner discussions — evaluating APIs, reviewing technical documentation, and negotiating integration design with external teams.
• Communicate architecture decisions and design trade-offs clearly to both technical and non-technical stakeholders; produce written decision records for all significant architectural choices.
• Mentor junior engineers and analysts on the CDP team — architecture thinking, engineering discipline, AI tooling adoption, and CI/CD practices.

Requirements

• Required
• A minimum of six (6) years of experience in solution architecture, data engineering, or senior software engineering roles — with at least two (2) years in a martech, CRM, or customer data platform context
• Demonstrated experience designing and delivering production applications or data platforms end-to-end — from architecture through deployment — in a cloud-native environment (Azure preferred)
• Strong data architecture skills — dimensional modeling, integration pattern design, API contract definition, and data quality framework development
• Hands-on Snowflake experience — including pipeline design, schema development, query optimization, data sharing, and external function patterns in a production data warehouse environment
• Hands-on Databricks experience — building and operating data engineering workloads using PySpark or SQL notebooks, Delta Lake, and Unity Catalog in an Azure-hosted environment
• Hands-on experience with Azure data engineering tools — including Azure Data Factory, Azure Functions, Azure Event Hubs, and Azure Blob Storage — for pipeline orchestration, event-driven processing, and data movement at scale
• Experience with Apache Airflow (or Azure-managed equivalent) for workflow orchestration — DAG design, dependency management, scheduling, and operational monitoring of data pipelines
• Full-stack development awareness: ability to design and contribute to front-end interfaces, back-end APIs, and data integration layers; direct coding experience in at least two tiers
• Hands-on experience with GitHub for source control, CI/CD pipeline configuration (GitHub Actions or equivalent), pull request workflows, and release management
• Demonstrated AI-native working practice — active use of AI coding assistants, code generation tools, or LLM-based development workflows in professional delivery contexts
• Experience designing integrations between campaign management, CRM, marketing automation, or customer engagement platforms and enterprise data platforms
• Strong written and verbal communication skills — architecture diagrams, design documents, and stakeholder presentations to VP and executive audiences
• Comfort with Agile/Scrum delivery — User Story authoring, sprint planning participation, and backlog management within an ADO or equivalent framework
• Direct experience building or evolving a custom campaign management, marketing orchestration, or customer engagement application
• Experience designing or implementing AI-assisted product features — recommendation engines, intelligent segmentation, natural language interfaces, or LLM-powered workflows — in a production martech or CDP context
• Familiarity with hospitality or gaming industry data domains — guest profiles, loyalty programs, player development, hotel operations, or reinvestment analytics
• Experience with enterprise integration platforms (MuleSoft, Azure API Management, or equivalent) for martech and CRM data flow orchestration
• Knowledge of infrastructure-as-code tools (Bicep, Terraform) for Azure-hosted application and data platform components
• Experience with observability tooling — application monitoring, pipeline alerting, and data quality dashboards — in a production data engineering context

Skills

Required

• Solution architecture
• Data engineering
• Full-stack development
• Snowflake
• Databricks
• Apache Airflow
• GitHub
• CI/CD
• AI coding assistants
• LLM-based development workflows
• Integration design
• Communication

Preferred

• AI-assisted product features
• Observability tooling