Top Remote Data Architect Jobs

Lead enterprise data architecture across warehouses, data lakes, downstream applications, and AI workloads. Design data models, Medallion Architecture, ELT standards, semantic layers, APIs, access controls, and reusable datasets. Partner with business and BI teams to drive adoption, conduct architecture reviews, establish platform standards, and mentor data and analytics engineers. The role requires deep Snowflake, dbt, data modeling, pipeline, and semantic-layer expertise, with experience in AI data patterns and cloud infrastructure preferred.

Designs and delivers Snowflake data architectures on AWS, including dbt-based data engineering and AI-enabled solutions. Leads RAG, vector search, embedding, semantic search, and analytics initiatives from proof of concept through production. Directs project teams, oversees solution quality and budgets, supports proposals and business cases, and serves as a trusted technical advisor to clients. Requires strong data engineering, governance, documentation, communication, and stakeholder-management skills, with travel as needed.

Lead complex data analyses, develop statistical models, write advanced SQL, build data models and dashboards, and establish technical standards for analytics. Mentor analysts, troubleshoot data quality and modeling challenges, partner with data engineering, translate ambiguous business questions into rigorous analyses, and communicate findings to senior stakeholders. Own the analytics function’s technical roadmap while ensuring data privacy and security practices.

Lead customer-facing architecture for AI data platforms and AI Factory environments. Design data-centric solutions for RAG, inference, and training workflows; size GPU and storage environments; optimize data movement, retrieval, latency, throughput, cost, and reliability. Partner with customers, sales, and Product Management to qualify opportunities, influence product roadmaps, create reference architectures, and develop scalable go-to-market solutions.

Designs and delivers Microsoft Fabric, Azure AI, Power BI, and generative AI architectures for enterprise clients. Leads solution design, prototyping, implementation readiness, governance, quality assurance, and client enablement across complex engagements. Develops AI solution patterns including RAG, vector search, embeddings, semantic search, and prompt orchestration. Leads project and client teams, supports business cases and proposals, reviews technical deliverables, manages risks, and serves as a trusted technical advisor.

Design and govern end-to-end data architecture for an integrated supply-chain e-commerce platform: create data models, build scalable ETL/ELT pipelines, ensure GDPR-compliant data governance, enable real-time analytics, optimize databases, plan retention/archival, and prepare data for LLM fine-tuning and RAG use cases.

Define and lead enterprise data platform architecture across ingestion, storage, processing, governance, and consumption. Establish standards for data modeling, lifecycle management, cloud infrastructure, security, networking, and cost optimization. Partner with engineering, analytics, ML, and business teams while leading large-scale data platform initiatives. The role requires expertise in modern data platforms, lakehouse architectures, streaming systems, governance, lineage, catalogs, and scalable data engineering.

Develops and supports data architecture and medical imaging informatics solutions on the XNAT platform. Responsibilities include data integration, modeling, storage and delivery optimization, data-sharing, coding, testing, release management, defect reduction, technical documentation, and partner support. The role collaborates with research and clinical teams, oversees stable XNAT releases, and advances platform capabilities. Required qualifications include a bachelor’s degree or equivalent experience, five years of data-related experience, and PostgreSQL skills.

Design and maintain scalable HR data architecture in Snowflake; build and optimize ETL/ELT pipelines with Matillion; enforce data governance, quality, lineage and security; manage SQL Server metadata environments; implement DevOps for data assets; partner with stakeholders and support AI-ready semantics and reusable data products.

Design and implement enterprise data architectures supporting batch and real-time analytics, streaming pipelines, data warehouses, lakehouses, and generative AI applications. Build hands-on ETL/ELT solutions using Python and SQL, with Treasure Data/CDP, Kafka, Snowflake, Databricks, cloud services, CDC, and Debezium. Develop AI-ready pipelines, semantic search, RAG and vector-search infrastructure, and integrate enterprise data with modern AI platforms. This remote contract requires production-grade architecture experience rather than traditional ETL or BI maintenance alone.

Design and optimize Oracle CX data architectures, models, integrations, governance, and conversion processes. Lead large-scale ETL and migration initiatives from legacy systems, resolve complex data issues, establish quality frameworks, and collaborate with business and technical stakeholders. Provide technical leadership, conduct workshops, support project teams, and identify process improvements across Oracle CX, ERP, HCM, and third-party platforms.

Designs and governs enterprise data architecture across Oracle ERP, SCM, CX, and related applications. Leads data modeling, large-scale migration and conversion, integration architecture, data governance, quality management, and validation initiatives. Partners with business and technical stakeholders to define scalable solutions, leads architecture workstreams, mentors project teams, and supports Oracle Cloud implementations and digital transformation programs.

Designs, builds, and maintains scalable data pipelines, data models, and platform infrastructure supporting enterprise reporting, analytics, and AI-driven decisions. Responsibilities include ingesting SaaS application data, developing medallion architecture layers and dbt transformations, implementing data quality monitoring, enforcing governance and access controls, and delivering reusable semantic data assets. The role collaborates with analytics, BI, and business teams, participates in Agile delivery, evaluates modern data-stack tools, and promotes engineering excellence through testing, documentation, and code reviews.

Lead and own end-to-end delivery of enterprise Data & AI programs, define data strategy and architecture (lakehouse/warehouse), drive modernization, governance, data quality, metadata/lineage, and AI adoption. Manage client relationships, account growth, program budgets, delivery teams, and stakeholder communications to ensure on-time, on-budget outcomes.

Design and own an event-driven data architecture that moves healthcare claims data through AWS and Snowflake into fraud, waste, and abuse alerts. Establish architecture standards, approve material changes, build complex pipeline components, and ensure scalability, reliability, and compliance with PII/PHI constraints. Collaborate with data science, compliance, technical, and investigative teams while supporting government review timelines. This is a hands-on principal role requiring strong Python, SQL, AWS, Snowflake, and production streaming pipeline experience.

Designs and supports scalable cloud data warehouses for CMS healthcare fraud, waste, and abuse analytics. Responsibilities include data modeling, healthcare interoperability, secure HIPAA-compliant governance, lineage, testing, CI/CD, automation, workflow scheduling, and technical architecture. The role integrates claims and healthcare domains using Snowflake and industry standards while collaborating with stakeholders, documenting solutions, and leading warehouse project delivery.

Designs and implements scalable data architectures, ingestion and transformation pipelines, API integrations, cloud data solutions, and data governance practices. The role supports analytics, business intelligence, dashboards, and AI models while optimizing infrastructure performance and cost. Responsibilities include data modeling, security, quality, lineage, compliance, documentation, and collaboration with cross-functional stakeholders. Requires expertise in Microsoft Fabric, AWS and Azure data services, SQL, Python, Tableau, and modern ETL/ELT practices.

Lead client-facing architecture for Microsoft Data & AI solutions, translating business priorities into secure, governed, scalable architectures and modernization roadmaps. Design lakehouse, batch and streaming platforms using Fabric, Databricks, Azure, Power BI, and related governance tools. Architect AI-ready data platforms supporting generative AI, agentic AI, RAG, vector search, and enterprise data agents. Support presales, proposals, estimation, executive presentations, implementation guidance, and mentorship of architects and engineering teams.

Leads corporate data architecture, standards, and data modeling for database and business intelligence projects. Designs logical and physical data models, source-to-target mappings, data warehouses, and integration architectures. Manages master and reference data initiatives, metadata repositories, data dictionaries, governance policies, data quality rules, and golden records. Provides technical leadership, mentoring, documentation, project guidance, and support throughout the systems development lifecycle.

Lead enterprise data architecture across AWS and Databricks Lakehouse: design scalable data models, ensure governance and data quality, mentor teams, evaluate and adopt data technologies (Delta Lake, Unity Catalog), oversee large projects and deliverables, and incorporate generative AI to accelerate data modeling and delivery.

Defines enterprise data architecture strategy while building scalable ELT/ETL pipelines, governed data products, analytics-ready models, and cloud data platforms. Leads BigQuery optimization, orchestration, data quality, observability, CI/CD, infrastructure-as-code, modernization, governance, privacy, security, and integration standards. Partners with executives and cross-functional teams to establish architecture direction, data contracts, domain models, and multi-year roadmaps while mentoring senior technical staff.

Designs and implements Google Cloud database migration strategies, including architecture, data models, database objects, pipelines, orchestration, security, and data transfers. Advises customers on Cloud Spanner and Cloud SQL implementations, translates SQL and stored procedures, configures Google Cloud components, performs performance testing, resolves performance issues, and delivers technical workshops and architectural recommendations.

Design scalable human-data collection systems for AI model post-training, including task frameworks, data workflows, quality assurance, evaluation datasets, and delivery pipelines. Partner with engineering and product teams to improve throughput and reliability while preserving data quality. Translate collection and quality patterns into insights that improve project design, client deliverables, and product development.

Designs and leads enterprise data architecture, including warehouses, lakes, lakehouses, data models, integration pipelines, governance, security, and analytics platforms. Partners with engineering, analytics, application, and business teams to create scalable cloud data ecosystems. Responsibilities include technology evaluation, migration and modernization strategy, architecture reviews, documentation, data quality, lineage, metadata, and technical mentorship.

Lead data architecture for NAVSUP OIS, including database design, data modeling, warehouse strategy, ETL/ELT pipelines, migration, security, retention, backup, recovery, and performance optimization. Coordinate cloud and containerized environments, guide data engineers and DBAs, and support modernization from Oracle to cloud and analytical platforms. The role also provides technical leadership, architecture planning, integration, risk assessment, and policy development for mission-critical systems.