Top Remote Data Architect Jobs

The Azure Data & AI Architect will design and deliver data and AI solutions using Azure technologies, manage project delivery, and communicate effectively with clients.

As a Solution Architect, you will define and lead data platform deliveries, manage implementation teams, translate requirements into designs, and mentor team members.

Serve as the technical authority for the Enterprise Data Platform, defining enterprise data architecture, canonical data and semantic models, externalization and API principles, and AI/knowledge-graph ready designs. Lead solution architecture, govern semantic layers and Collibra integration, chair architecture reviews, shape technology roadmap, and partner with platform and trust teams to ensure consistent, governed data products across delivery teams.

Lead cross-discipline delivery and practice maturation across data engineering, visualization, governance, and AI. Orchestrate unified delivery approaches, set delivery standards and accelerators, coach architects, apply AI to improve delivery, and drive adoption of practice capabilities while managing stakeholder relationships and delivery risk on large consulting engagements.

Design, build, test, deploy, document, and maintain Power BI reporting solutions and semantic models. Develop SQL queries, data pipelines, and ETL processes; assess platform performance and governance; implement security and access controls; create monitoring code and documentation; collaborate with IT leadership and data management to support reporting strategy and provide training and technical support.

Design and implement scalable batch and near-real-time data pipelines and storage across AWS. Define event-driven integrations, ensure data quality and observability, optimize performance, lead production troubleshooting and migrations, review designs and CI/CD practices, and collaborate with architects and stakeholders to align solutions to business needs.

Design and implement scalable batch and near-real-time data pipelines and storage on AWS. Define data architecture, ensure observability, optimize performance, lead production troubleshooting and migrations, enforce CI/CD and deployment best practices, and collaborate with stakeholders and platform teams to align solutions with business needs.

Lead technical strategy with channel partners across ASEAN to design, validate, and scale Microsoft Cloud and Data & AI solutions. Drive offer development, run workshops and POCs, enable specializations, and translate technical work into repeatable, revenue-generating partner services aligned to Microsoft Cloud and AI priorities.

Customer-facing technical leader who architects, builds, and optimizes large-scale data engineering and lakehouse solutions on Databricks. Supports pre-sales and delivery with POCs, performance/load testing, workload sizing, migrations, observability, and adoption through workshops and community engagement.

Lead design and hands-on implementation of scalable data platforms on AWS and Snowflake. Build pipelines, storage layers, and data models; define ingestion, transformation, and quality standards; optimize performance and cost; ensure security and compliance; mentor engineers and partner cross-functionally to deliver production-ready, AI-ready data solutions.

Architect and deploy scalable Inventa data-management solutions, lead complex enterprise integrations (APIs, on-prem, cloud, SaaS, data lakes), resolve architectural performance and access issues, create global architecture guides and documentation, and feed product improvements through field insights. Customer-facing, cross-functional collaboration with Sales, Customer Success, and Product.

Senior AWS-focused cloud solution architect who defines and scales AI & Data delivery: builds reference architectures, presales S OWs/LOEs, provides technical SME support, and performs delivery oversight and architecture reviews to ensure repeatable, governed enterprise AI and data platform outcomes.

Lead architecture and evolution of enterprise-scale data, analytics, and MLOps platforms. Design AI-ready data foundations, semantic/ontology and federated catalog solutions, APIs and permission-aware discovery. Drive adoption through reference architectures, prototyping, R&D, and mentorship while ensuring security, governance, cost efficiency, and cloud-native operational excellence.

Serve as the technical bridge between GenLogs' data platform and customers: discover high-value use cases, communicate capabilities, build rapid prototypes/POCs (SQL, Snowflake, Python/Streamlit), translate requirements, and guide engineering/data teams to production while traveling to customers ~1-2 times/month.

Lead app and data modernization strategy for Cloud+AI customers: design repeatable, code-based IP and automation, modernize .NET/Java apps and databases to Azure platform services, enable field teams with agentic tooling and skilling, unblock escalations, and influence product roadmap for large-scale modernization.

Lead Data & AI technical strategy and pre-sales engagements on Microsoft Azure. Drive discovery, architecture, and executive presentations to translate business needs into scalable Data & AI solutions. Mentor architects and data scientists, ensure alignment with security and governance, and expand pipeline through workshops, POVs, and thought leadership.

Lead pre-sales data engineering engagements to design and implement scalable lakehouse data platforms (Snowflake/Databricks). Advise customers on data strategy, governance, pipelines, AI-ready foundations, migrations, and translate technical solutions into business outcomes while supporting sales and partner enablement.

Design and lead end-to-end enterprise data and GenAI architectures, drive cloud migration/modernization, define GenAI/Copilot/agentic patterns (RAG, embeddings, vector search), establish MLOps/LLMOps and governance, ensure Responsible AI and security, and act as trusted technical advisor across presales through production.

Lead design, build, and maintain scalable ETL pipelines and Databricks Lakehouse solutions. Standardize disparate datasets, implement CI/CD and reproducible code practices, integrate Databricks with enterprise systems, mentor engineers, document requirements, and collaborate with cross-functional teams to deliver secure, automated data platform capabilities.

Lead enterprise data architecture and governance across Microsoft Fabric, Azure, EDW and analytics. Define integration patterns, data modeling, lineage, security, DR/continuity and vendor standards. Guide engineering teams on implementation, performance tuning, cost optimization, testing, and responsible AI/GenAI integration. Ensure solutions meet regulatory, audit, and operational requirements while collaborating with business and IT stakeholders to translate needs into long-term platform roadmaps.

Design and advise on high-performance AI data infrastructure and storage solutions. Assess AI data readiness, map NVIDIA AI Data Platform integrations, guide data pipeline and lakehouse architectures, lead presales engagements, develop partner reference architectures, deliver workshops and enablement, and support field teams on enterprise AI workload outcomes.

The Associate Data/EDI Architect will manage implementation projects for employee benefits software, mentor teammates, and ensure quality execution while cultivating customer relationships.

Design, support, and improve data analytics solutions (OAC and Power BI). Manage data ingestion, ETL, data quality, access, and documentation. Liaise with stakeholders, migrate dashboards from OAC to Power BI, and follow SDLC and project management practices.

Customer-facing cloud solution architect for Azure data platforms and AI readiness. Lead architecture design, proofs-of-concept, and production deployments focused on performance, resiliency, security, and measurable business value using Microsoft Fabric, Azure Databricks, Cosmos DB, and Purview.

Design and maintain logical and physical data models for the EMR, define system integration and migration specifications, collaborate with clinical and IT stakeholders, ensure interoperable interfaces, drive metadata documentation, mentor on testing and release processes, and support architecture reviews and implementations with minimal supervision.