AI Data Architect

3Pillar Global

AI Data Architect to design and govern the AI data platform at 3Pillar, facilitating AI initiatives across various business sectors.

Posted 7/25/2026full-timeRemote • 🇺🇸 United StatesSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and owning enterprise AI data platforms, with a focus on designing scalable data architectures and developing automated data pipelines. Proficient in modernizing legacy systems to cloud-native solutions while ensuring data quality and compliance.

Highest-signal resume keywords

Data EngineeringAI Data Platform ArchitectureETL Process DevelopmentReal-Time StreamingCloud Storage and Compute

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Architecture PatternsETL in PythonETL in JavaETL in ScalaDatabricksSnowflakeKafkaSpark Structured StreamingPySparkDelta Lake

Tools & Technologies

AWSAzureMLflowCI/CD for AIAutomated Evaluation

Industry Keywords

AI WorkloadsData LakeData WarehouseData MeshEvent-Driven ArchitectureVector StoresSemantic ModelsKnowledge GraphsRetrieval InfrastructureLegacy Pipeline Modernization

Tech Stack

Tools & technologies

AWSAzureCloudETLJavaKafkaPySparkPythonScalaSpark

About the role

Key responsibilities & impact

Architect and own the enterprise AI data platform — the unified, governed layer that ingests, transforms, stores, and serves all data consumed by AI systems across the organisation.
Design multi-domain data models (lakehouse, data mesh, event-driven) that are structured from day one to serve AI workloads: clean lineage, versioned schemas, well-documented contracts, and low-latency serving APIs.
Define tools & technologies to develop automated data pipelines, write ETL processes, develop dashboard & report and create insights

Requirements

What you’ll need

15+ years of hands-on data engineering and architecture experience, alongside building production AI/ML and LLM-era data infrastructure.
Strong Experience with either Databricks or Snowflake; experience with both is desirable.
Strong data architecture patterns & principles, ability to design secure & scalable data lakes, data warehouse, data hubs, and other event-driven architectures
Expertise in designing and writing ETL processes in Python / Java / Scala
Own the full data stack: real-time streaming (Kafka, Spark Structured Streaming), batch processing (Databricks, PySpark, Delta Lake), cloud storage and compute (AWS, Azure), and data quality /metadata management.
Drive modernisation of legacy pipelines (on-prem ETL, batch DWH) to cloud-native, AI-ready architectures with measurable improvements in cost, latency, and delivery velocity.
Proven experience designing enterprise-scale AI data platforms that serve multiple AI consumers —not just one application or pipeline.
Hands-on experience with vector stores, semantic models, knowledge graphs, and retrieval infrastructure in production environments.
Working knowledge of LLMOps: model serving pipelines, MLflow, CI/CD for AI, automated evaluation, and production monitoring.

Benefits

Comp & perks

Medical Insurance benefits as per company policy.
Dental insurance as per company policy.
Vision insurance as per company policy.
Employer paid Disability, Life, and AD&D insurance
Unlimited PTO
Paid parental leave
401K
Flexible work policy
12 Paid Holidays