Data Solutions Architect

Bright Vision Technologies•Artesia, CA
•$150,000 - $180,000•Remote

About The Position

Bright Vision Technologies is seeking a Data Solutions Architect to define and lead the architecture of its enterprise data platform across ingestion, storage, processing, governance, and consumption. The role drives the strategic direction of data infrastructure, establishes standards for data modeling and lifecycle management, and partners with data engineering, analytics, machine learning, and business stakeholders to deliver a coherent, scalable data foundation. The ideal candidate brings deep technical expertise in modern data platforms, sound architectural judgment, and experience leading substantial data-platform programs. This is a full-time, direct W-2 position that is 100% remote within the United States. The listed salary range is $150,000–$180,000 annually. The company encourages applications from U.S. citizens, Green Card holders, EAD holders, and candidates seeking an H-1B transfer; it cannot sponsor new H-1B visa petitions for this position.

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 production-scale experience with Spark, Flink, or Kafka.
  • Strong data modeling expertise across dimensional, normalized, and data-vault patterns.
  • Experience implementing governance, lineage, and catalog capabilities.
  • Solid understanding 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.

Nice To Haves

  • 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, such as Snowflake, Databricks, AWS, Azure, or GCP certifications.
  • Public talks or writing on data architecture.

Responsibilities

  • Define and lead the architecture of the enterprise data platform across ingestion, storage, processing, governance, and consumption.
  • Drive the strategic direction of data infrastructure.
  • Set standards for data modeling and lifecycle management.
  • Partner with data engineering, analytics, machine learning, and business stakeholders to deliver a coherent, scalable data foundation.
  • Lead large data-platform initiatives across teams.
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