job summary
Enterprise Healthcare client has an immediate opening for a highly motivated Lead Cloud Architect to join their dynamic and growing team. All qualified candidates are encouraged to apply!
location: Telecommute
job type: Contract
salary: $73.16 - 83.16 per hour
work hours: 8am to 5pm
education: Bachelors
responsibilities
Key Responsibilities
Enterprise Architecture and Strategy
Define and maintain the enterprise Salesforce Data Cloud architecture roadmap, reference architecture, principles, and reusable patterns.
Translate business capabilities and nonfunctional requirements into scalable solution architectures, transition states, and implementation guardrails.
Assess platform fit, integration complexity, data-volume considerations, technical debt, performance, operability, and total lifecycle impact.
Lead architecture review boards, solution-design reviews, proofs of concept, and technical risk assessments.
Data Modeling, Identity and Governance
Design Data Cloud data streams, Data Lake Objects, Data Model Objects, data mappings, data spaces, harmonization rules, and lifecycle controls.
Lead identity-resolution strategy, match and reconciliation rules, unified profiles, source prioritization, survivorship, and data-quality controls.
Define metadata, lineage, stewardship, retention, consent, privacy, and master-data-management alignment.
Establish controls for PII and PHI handling, access segregation, encryption, monitoring, auditability, and regulated-data use.
Integration and Data Engineering
Architect batch, near-real-time, streaming, event-driven, API-led, and zero-copy integration patterns across Salesforce and enterprise platforms.
Define ingestion, transformation, error handling, replay, idempotency, reconciliation, observability, and recovery standards.
Partner with MuleSoft, Snowflake, Azure, data engineering, and source-system teams to establish interface contracts and canonical models.
Guide large-volume data processing, performance tuning, capacity planning, data archival, and cost-aware design.
Analytics, Activation and AI Enablement
Design trusted segmentation, Calculated Insights, activation targets, Data Actions, reporting, dashboards, and analytical consumption patterns.
Enable CRM Analytics, Tableau, personalization, marketing, service, sales, Agentforce, predictive modeling, and generative AI use cases.
Define grounding, retrieval, prompt-data, model-access, audit, and responsible-AI controls for customer-data use cases.
Ensure activated audiences and insights are measurable, traceable, governed, and aligned to approved business purposes.
Delivery Leadership
Collaborate with product owners, business analysts, security, privacy, data governance, architects, developers, administrators, and vendors.
Provide technical direction throughout discovery, backlog refinement, design, build, testing, deployment, hypercare, and operational transition.
Mentor architects and engineers, establish engineering standards, and conduct code, configuration, data-model, and integration reviews.
Define architecture deliverables, quality gates, technical acceptance criteria, and production-readiness requirements.
Required Coding Languages and Development Skills
The architect should be capable of reviewing production code, creating prototypes, validating integration designs, troubleshooting complex data flows, and setting engineering standards. The role is architecture-led but requires meaningful hands-on depth.
Required capabilities include
- SQL and SOQL
- Expected Depth: Advanced
- Complex query design
- Joins and subqueries
- Common table expressions
- Window functions
- Aggregations
- Data transformations
- Data reconciliation
- Query-performance analysis
- Snowflake SQL
- Data Cloud query development
- Calculated Insights
- Salesforce CRM data retrieval
- SOQL relationship queries
- Large-volume query optimization
- Apex
- Expected Depth: Strong
- Service-layer design
- Apex classes and interfaces
- Triggers and trigger frameworks
- Batch Apex
- Queueable Apex
- Scheduled Apex
- Future methods
- Asynchronous processing
- Platform Event processing
- Change Data Capture consumers
- External-service callouts
- Bulkification
- Governor-limit management
- Salesforce security enforcement
- Error handling and logging
- Unit-test design
- Mocking and dependency isolation
- Code-review standards
- JavaScript and TypeScript
- Expected Depth: Strong
- Lightning Web Components
- Modern JavaScript
- TypeScript-based development
- Reusable user-interface components
- Browser-side integration
- Node.js tooling
- API clients
- Asynchronous programming
- JSON processing
- Component testing
- Front-end test automation
- Client-side security
- Performance optimization
- Accessibility-aware development
- Python
- Expected Depth: Working to Strong
- Data profiling
- Data-quality validation
- Data transformation utilities
- API automation
- Test-data generation
- Operational scripts
- Reconciliation utilities
- Data engineering prototypes
- Jupyter notebooks
- AI and machine-learning integration
- Model-service integration
- File processing
- Production diagnostics
- Automation of repeatable architecture-validation tasks
- DataWeave
- Expected Depth: Strong
- MuleSoft payload transformation
- Canonical data-model implementation
- Mapping between source and target schemas
- JSON, XML, CSV, and structured-file processing
- Data validation
- Transformation rules
- Error handling
- Null and default-value management
- Reusable transformation modules
- Lookup and enrichment logic
- Performance-aware transformations
- Unit testing of transformation logic
- Java
- Expected Depth: Working
- Review of Java-based enterprise services
- MuleSoft extensions
- Custom connectors
- Enterprise integration services
- Object-oriented programming
- Review of middleware components
- JVM-based application diagnostics
- API implementation patterns
- Secure coding concepts
- Error handling and logging
- Shell and PowerShell
- Expected Depth: Working
- CI/CD automation
- Deployment scripting
- Environment validation
- Secure operational utilities
- File and configuration management
- Automated testing support
- Troubleshooting scripts
- Build-pipeline integration
- Release verification
- Platform health checks
- HTML and CSS
- Expected Depth: Working
- Lightning Web Component presentation
- Semantic HTML
- Responsive design
- Accessibility
- CSS styling
- Salesforce Lightning Design System alignment
- Experience Cloud extension review
- Cross-browser considerations
Optional Languages
Experience with one or more of the following is preferred:
Scala
Spark SQL
C#
R
Required Technology Stack
Salesforce Data Cloud
The architect should have strong experience with:
Data Streams
Data Lake Objects
Data Model Objects
Source-to-target data mappings
Customer 360 data models
Data harmonization
Data Spaces
Identity resolution
Identity match rules
Identity reconciliation rules
Unified Individual Profiles
Unified account and household concepts
Segmentation
Calculated Insights
Activation Targets
Data Actions
Batch ingestion
Streaming ingestion
Data Cloud APIs
Zero-copy integration concepts
Data Cloud monitoring
Usage governance
Consent and privacy alignment
Data lifecycle management