Principal Data Architect / Enterprise Data & AI Architect

INFOLOB Global, Inc.

Irving, United States of America

6 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Languages

English

Experience level

Senior

Job location

Irving, United States of America

Tech stack

Artificial Intelligence

Amazon Web Services (AWS)

Data analysis

ARM

Azure

Big Data

Cloud Computing

Cloud Database

Cloud Engineering

Data Architecture

Information Engineering

Data Governance

Data Sharing

Dataspaces

Data Vault Modeling

Information Systems Security Architecture Professional

Python

Knowledge-Based Systems

Meta-Data Management

Role-Based Access Control

Cloud Services

SQL Databases

Data Streaming

Enterprise Data Management

Google Cloud Platform

Retrieval-Augmented Generation

Large Language Models

Snowflake

Spark

IT Architecture

Multi-Cloud

Generative AI

Data Strategy

Data Lake

Star Schema

Google BigQuery

Data Management

Machine Learning Operations

Stream Processing

Oracle Cloud Infrastructure

Data Pipelines

Serverless Computing

Redshift

Databricks

Job description

We are seeking a highly experienced Senior Principal Data Architect / Enterprise Data & AI Architect to lead the design and execution of enterprise-scale data platforms and AI-driven solutions. This role will define the organization's data strategy, architecture, and AI enablement roadmap, driving transformation across cloud, analytics, and next-generation AI capabilities., 1. Strategy & Architecture

Define and drive enterprise data strategy aligned with business, analytics, and AI objectives
Design scalable lakehouse and modern data platform architectures supporting batch and real-time data processing
Establish architectural standards, patterns, and best practices across the organization

AI & Advanced Analytics Enablement

Build model-ready data foundations to support AI/ML and Generative AI use cases
Design and implement agentic AI workflows using frameworks such as LangChain
Architect RAG (Retrieval-Augmented Generation) pipelines and enterprise knowledge systems
Enable data pipelines optimized for model training, inference, and LLM integration

Platform Engineering & Cloud Architecture

Lead end-to-end data platform modernization and cloud migration initiatives
Architect and optimize solutions across leading platforms: o Snowflake: Multi-layer architecture (raw, curated, consumption), RBAC, data sharing, Cortex AI o Databricks: Lakehouse architecture, Delta Lake, ML lifecycle, model serving o Google BigQuery & Amazon Redshift: Serverless analytics and native integrations
Optimize pipelines for performance, scalability, and cost efficiency across cloud ecosystems (AWS, Azure, Google Cloud Platform)

Data Governance & Security

Establish enterprise-wide frameworks for data quality, observability, metadata management, and lineage
Define and enforce data governance policies and standards
Implement secure architectures including encryption, access controls, and compliance (e.g., HIPAA, SOC 2)

Leadership & Stakeholder Engagement

Serve as a trusted advisor and technical authority to executive and senior stakeholders
Lead and mentor data engineering, analytics, and AI teams
Drive cross-functional collaboration across business, technology, and operations

Requirements

o 8-10+ years in data engineering / data architecture o 3-5+ years in cloud data platforms and modern data ecosystems

Technical Expertise:

o Strong proficiency in SQL, Python, and Spark o Deep experience in data modeling (Relational, Star Schema, Snowflake Schema, Data Vault) o Hands-on experience with batch and streaming architectures

Cloud Platforms:

o Expertise in at least two: AWS, Azure, OCI, Google Cloud Platform o Multi-cloud exposure is highly preferred

Preferred Qualifications

Certifications such as:

o SnowPro Core (Snowflake) o Cloud Data Engineer certifications (AWS, Azure, Google Cloud Platform)

Proven, hands-on experience in at least two of the following platforms:

o Snowflake o Databricks o Google BigQuery o Amazon Redshift