Enterprise Data Architect – AI Solutions Leader

AnswerRocket

Enterprise data architect leading lakehouse and generative AI solutions for Fortune 2000 companies. Designing scalable data platforms, RAG systems, and enterprise transformation initiatives.

Posted 8/4/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data architecture and engineering, with a strong focus on cloud-native data lakehouse platforms, AI/ML solutions, and advanced analytics. Proven ability to lead technical teams, mentor professionals, and effectively communicate complex concepts to executive stakeholders.

Highest-signal resume keywords

Data ArchitectureCloud-Native Data Lakehouse PlatformsAI/ML SolutionsDatabricksETL/ELT Pipelines

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringEnterprise ArchitectureMLOpsPythonSQLTypeScriptCloud Serverless ArchitecturesReal-Time Data PipelinesBatch Data PipelinesAdvanced Analytics

Soft Skills

Excellent CommunicationMentoringInfluencing StakeholdersClient DiscoveryBusiness Acumen

Tools & Technologies

DatabricksSnowflakeBigQueryTableauPower BIDelta LakeUnity CatalogAWSGCPAzure

Industry Keywords

Data GovernanceObservability EngineeringMedallion ArchitectureGenerative AIRAG Pipelines

Tech Stack

Tools & technologies

AWSAzureBigQueryCloudETLGoogle Cloud PlatformPythonSQLTableauTypeScriptUnity

About the role

Key responsibilities & impact

Lead data strategy initiatives, including current-state assessments, enterprise architecture design, and governance frameworks
Design and implement cloud-native data lakehouse platforms using Databricks, Snowflake, and BigQuery with medallion architectures
Build real-time and batch data pipelines using ETL/ELT, streaming, and orchestration technologies
Architect and develop generative AI solutions, including RAG pipelines, multi-agent systems, and autonomous monitoring
Create advanced analytics and BI solutions with Tableau and Power BI
Lead technical teams, mentor data professionals, and drive innovation lab initiatives
Conduct client discovery sessions and translate complex technical concepts for executive audiences
Implement MLOps, feature stores, and AI/ML pipelines with performance optimization
Establish data governance standards, quality monitoring, and observability engineering practices

Requirements

What you’ll need

Bachelor's degree in Computer Science, Data Engineering, or related field, or equivalent industry experience
10+ years of experience in data architecture and engineering
5+ years of experience in enterprise-scale systems
3+ years of experience with AI/ML platforms
Hands-on expertise with Databricks, Delta Lake, Unity Catalog, and modern data lakehouse architectures
Proficiency in TypeScript, Python, SQL, and cloud serverless architectures across AWS, GCP, or Azure
Experience building RAG pipelines, vector databases, LLM operations, and multi-model AI systems
Proven ability to lead technical teams and influence enterprise stakeholders
Ability to architect solutions and write production code
Excellent communication skills and ability to present to C-level executives and translate business requirements
Proven consulting experience with enterprise clients and strong business acumen
Applicants must reside in the United States
Must be authorized to work in the U.S.
Must not require employer sponsorship now or in the future

Benefits

Comp & perks

Competitive salary with opportunities for growth and recognition
Health, dental, and vision insurance
Paid Time Off
Parental leave
Flexible remote-first work environment
Additional opportunities for in-person meetups
Professional growth and skills development
Mentorship
High-visibility projects and career-building opportunities