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, while partnering with data engineering, analytics, ML, and business stakeholders to deliver a coherent, scalable data foundation. The position calls for deep technical expertise in modern data platforms, architectural judgment, and experience leading data-platform programs of meaningful scope and complexity. This is a full-time, direct W2 position that is 100% remote within the United States, with an annual salary range of $135,000–$170,000. The posting gives an experience requirement of 6+ years in the position summary and specifies eight or more years of data engineering experience in the required qualifications.
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.
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 in Snowflake, Databricks, AWS, Azure, or GCP.
- Public talks or writing on data architecture.
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 using 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 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 drive ownership, quality, and decoupling between producers and consumers.
- Lead architecture reviews and guide 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.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
