AI Data Platform Engineer

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. The AI Data Platform Engineer will define and lead the architecture of the enterprise data platform across ingestion, storage, processing, governance, and consumption layers. The role also establishes data standards, guides platform integrations and security, leads architecture reviews, optimizes costs, and mentors data engineering teams.

Responsibilities:

• Define the target-state architecture of the enterprise data platform, including ingestion, storage, processing, and consumption layers
• Establish standards for data modeling, schema evolution, partitioning, file formats, and storage organization
• Architect lakehouse, warehouse, and streaming patterns leveraging technologies such as Snowflake, Databricks, BigQuery, Redshift, Iceberg, Delta Lake, or Hudi
• Design end-to-end data pipelines that balance latency, cost, reliability, and maintainability across batch and streaming workloads
• Lead the integration of governance, lineage, and catalog tools such as Collibra, Alation, Atlan, Unity Catalog, or DataHub
• Define security architecture including row- and column-level controls, masking, encryption, and identity-aware access patterns
• Partner with ML, BI, and product teams to ensure platform capabilities align with downstream consumption needs
• Establish data contract and data product principles to drive ownership, quality, and decoupling between producers and consumers
• Lead architecture reviews and provide guidance on pipeline and warehouse design proposals across teams
• Drive cost optimization and capacity planning across the data platform estate
• Design disaster recovery, multi-region, and high-availability strategies for critical data assets
• Mentor data engineers and architects on platform standards and emerging best practices
• Produce architecture artifacts including context diagrams, decision records, and reference patterns
• Stay current with data platform research, vendor offerings, and open-source ecosystem developments

Requirements:

• • Bachelor's or Master's degree in Computer Science, Information Systems, or a related field
• • Eight or more years of experience in data engineering, with significant time in architecture roles
• • Deep expertise across at least two major data platforms such as Snowflake, Databricks, BigQuery, or Redshift
• • Strong understanding of lakehouse architectures, modern table formats, and streaming systems
• • Hands-on experience with Spark, Flink, or Kafka at production scale
• • Strong data modeling expertise across dimensional, normalized, and data-vault patterns
• • Experience implementing governance, lineage, and catalog capabilities
• • Solid grasp of cloud platforms, networking, identity, and cost optimization
• • Excellent communication, facilitation, and stakeholder management skills
• • Track record of leading large data platform initiatives across teams
• • Experience with data mesh or data product architectures
• • Familiarity with semantic layers such as dbt Semantic Layer, Cube, or LookML
• • Exposure to regulated industries with strict data residency or audit requirements
• • Cloud or platform certifications (Snowflake, Databricks, AWS, Azure, GCP)
• • Public talks or writing on data architecture