AI Data Platform Engineer
About The Position
Bright Vision Technologies is seeking an AI Data Platform Engineer to define and lead the architecture of its enterprise data platform across ingestion, storage, processing, governance, and consumption. The role sets the strategic direction of data infrastructure and standards for data modeling and lifecycle management, partnering with data engineering, analytics, machine learning, and business stakeholders to build a coherent, scalable data foundation. The engineer will apply deep technical expertise and architectural judgment to platform programs of significant scope and complexity. The position is full-time, direct W2, and 100% remote within the United States. The stated salary range is $135,000–$170,000 annually.
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, including 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 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.
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 target-state architecture for 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 using platforms and technologies such as Snowflake, Databricks, BigQuery, Redshift, Iceberg, Delta Lake, or Hudi.
- Design end-to-end batch and streaming data pipelines that balance latency, cost, reliability, and maintainability.
- Lead 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 align platform capabilities with downstream consumption needs.
- Establish data contract and data product principles to support ownership, quality, and decoupling between producers and consumers.
- Lead architecture reviews and advise teams on pipeline and warehouse design proposals.
- 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.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
