Senior AI Data Platform Architect - Remote

Bright Vision Technologies; United States; Remote; USD 135,000 - 170,000; Full time

Job summary

Bright Vision Technologies is seeking an AI Data Platform Engineer to define and lead the architecture of our enterprise data platform, spanning ingestion, storage, processing, governance, and consumption layers.

You will set standards for data modeling, lifecycle management, and collaborate with data engineering, analytics, ML, and business stakeholders to deliver a scalable data foundation.

Qualifications

• Bachelor's or Master's degree in Computer Science, Information Systems, or related field.
• Eight+ years of experience in data engineering with architecture leadership.
• Deep expertise across Snowflake, Databricks, BigQuery or Redshift.

Responsibilities

• Define target-state architecture of the enterprise data platform.
• Establish standards for data modeling, schema evolution, and storage organization.
• Architect lakehouse, warehouse, and streaming patterns using Snowflake, Databricks, BigQuery, Redshift, Iceberg, Delta Lake, or Hudi.
• Design end-to-end data pipelines balancing latency, cost, and reliability.
• Lead governance, lineage, and catalog tool integration.
• Define security architecture including access controls and encryption.
• Partner with ML, BI, and product teams for platform alignment.
• Lead architecture reviews and guide cross-team design proposals.
• Drive cost optimization and capacity planning across the data platform estate.
• Design disaster recovery and multi-region high availability.

Skills

Data engineering

Data architecture

Cloud platforms

Spark / Kafka

Education

Bachelor's or Master's in CS/IS

Tools

Snowflake

Databricks

BigQuery

Redshift

Job description

Bright Vision Technologies is seeking an AI Data Platform Engineer to define and lead the architecture of our enterprise data platform, spanning ingestion, storage, processing, governance, and consumption layers.

You will set standards for data modeling, lifecycle management, and collaborate with data engineering, analytics, ML, and business stakeholders to deliver a scalable data foundation.