Lead Cloud Architect

About the role

Enterprise Healthcare client has an immediate opening for a highly motivated Lead Cloud Architect to join their dynamic and growing team.

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.

• SQL and SOQL – 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 – 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 – 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 – 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 – Strong: MuleSoft payload transformation, canonical data-model implementation, mapping between source and target schemas, JSON/XML/CSV/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 – 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 – 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 – 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 – 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, data quality and reconciliation.
• Salesforce Platform – Experience across relevant Salesforce clouds and platform capabilities, including: Sales Cloud, Service Cloud, Experience Cloud, Marketing Cloud, Health Cloud, applicable Salesforce industry clouds, Lightning Web Components, Apex, Salesforce Flow, Platform Events, Change Data Capture, Salesforce REST APIs, Salesforce SOAP APIs, Bulk API, Composite APIs, Named Credentials, External Credentials, Salesforce Shield, CRM Analytics, Tableau, Agentforce, Einstein capabilities. Working knowledge of OmniStudio is preferred, including OmniScripts, FlexCards, DataRaptors, and Integration Procedures.
• Integration and API Management – Strong experience with MuleSoft and enterprise integration technologies, including: MuleSoft Anypoint Platform, API-led connectivity (System APIs, Process APIs, Experience APIs), DataWeave, CloudHub, Runtime Fabric, API policies, API monitoring, API governance, reusable integration assets, canonical data models, interface contracts. Relevant API and security technologies include: REST, SOAP, GraphQL, OpenAPI, OAuth 2.0, JSON Web Tokens, SAML, Mutual TLS, Webhooks, event-driven integration, API contract testing. Relevant messaging and streaming technologies include: Salesforce Platform Events, Salesforce Change Data Capture, Apache Kafka, Azure Event Hubs, Azure Service Bus, and comparable enterprise messaging technologies.
• Data Platforms and Engineering – Experience with enterprise data platforms such as: Snowflake, Azure Data Lake, Azure Databricks, Azure Synapse, SQL Server, Oracle, and comparable enterprise data warehouses and lakehouse platforms. Relevant ETL, ELT, and orchestration technologies include: MuleSoft, Azure Data Factory, Informatica, SQL Server Integration Services, Databricks, Apache Spark, and comparable enterprise data-integration products. Relevant file formats and data-engineering concepts include: Parquet, JSON, XML, CSV, Avro, schema evolution, data partitioning, data observability, data lineage, data cataloging, data-quality tooling, data reconciliation, metadata management, batch-processing architecture, and streaming-processing architecture.
• DevSecOps and Engineering Toolchain – Hands-on familiarity with: Git, GitHub Enterprise, GitLab, Bitbucket, Azure DevOps, Jenkins, Salesforce CLI, scratch organizations, unlocked packages, Copado, Gearset, artifact repositories, and release-management platforms. Establish or oversee automation for: unit testing, integration testing, regression testing, performance testing, contract testing, data-quality testing, security testing, static code analysis, dependency scanning, deployment validation, post-deployment verification, and release governance. Experience with infrastructure and configuration automation is preferred, including platform-appropriate scripting, Terraform, and comparable infrastructure-as-code tooling.
• Cloud, Security and Operations – Experience with Microsoft Azure services such as: Microsoft Entra ID, Azure Key Vault, Azure API Management, Azure Functions, Azure Storage, Azure Monitor, Azure Log Analytics, Azure networking, private endpoints, and identity and access management. Comparable AWS or Google Cloud Platform experience may be considered. Required security capabilities include: role-based access control, least-privilege design, identity and access management, key management, secret management, encryption at rest, encryption in transit, tokenization, data masking, audit logging, security information and event management integration, incident support, disaster-recovery design, PII protection, PHI protection, consent management, data-retention controls, and data-access monitoring. Required operational architecture capabilities include: monitoring, distributed tracing, alerting, service-level objectives, operational dashboards, runbooks, capacity planning, performance management, production-support readiness, failure recovery, business continuity, disaster recovery, and cost monitoring and optimization.

Qualifications

• Bachelors degree
• AWS
• Application Architect
• AI
• Azure Data
• cloud environment
• Cloud
• Cloud Computing Security
• cloud-native architectures
• customer master data
• data solutions
• database technologies
• DevOps
• distributed cache
• enterprise data platforms
• enterprise application
• enterprise software
• ETL
• Google cloud
• AI architecture
• Identity and Access Management
• data engineering
• Information Systems
• computer science
• hosting services
• Jenkins
• Microservices
• Microsoft Azure
• Azure
• Azure Solutions
• MuleSoft
• multi-cloud
• Oracle Cloud Infrastructure
• retrieval-augmented generation
• Salesforce platform
• Salesforce Data Cloud
• Salesforce
• vector search
• Serverless
• Snowflake
• software design patterns
• technical design reviews
• System Architect
• integrating
• TOGAF
• identity resolution