AI Data Platform Engineer
New
B
Bright Vision TechnologiesData platforms
100% Remote (U.S.)Full-TimeSenior
Salary135,000 - 170,000 USD per year
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Job Details
- Experience
- 6+ years; Required Qualifications specify eight or more years of experience in data engineering, with significant time in architecture roles.
- Required Skills
- KafkaSnowflakeSparkData modelingBigQueryRedshiftDatabricks
Requirements
- Have a Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related field.
- Have eight or more years of data engineering experience, including significant time in architecture roles.
- Bring deep expertise in at least two major data platforms, such as Snowflake, Databricks, BigQuery, or Redshift.
- Understand lakehouse architectures, modern table formats, and streaming systems.
- Have production-scale hands-on experience with Spark, Flink, or Kafka.
- Bring data modeling expertise across dimensional, normalized, and data-vault patterns.
- Have experience implementing governance, lineage, and catalog capabilities.
- Understand cloud platforms, networking, identity, and cost optimization.
- Have a track record of leading large data-platform initiatives across teams.
- Preferred: experience with data mesh or data product architectures.
- Preferred: familiarity with semantic layers such as dbt Semantic Layer, Cube, or LookML.
- Preferred: exposure to regulated industries with data residency or audit requirements.
Responsibilities
- Define the target-state architecture of the enterprise data platform across ingestion, storage, processing, and consumption.
- Establish standards for data modeling, schema evolution, partitioning, file formats, and storage organization.
- Architect lakehouse, warehouse, and streaming patterns using relevant data-platform technologies.
- Design end-to-end data pipelines for batch and streaming workloads, balancing latency, cost, reliability, and maintainability.
- Lead integration of governance, lineage, and catalog tools.
- Define security architecture, including access controls, masking, encryption, and identity-aware access patterns.
- Partner with ML, BI, and product teams to align platform capabilities with downstream needs.
- Lead architecture reviews, cost optimization, and capacity planning across the data platform.
- Design disaster recovery, multi-region, and high-availability strategies for critical data assets.
- Mentor data engineers and architects and produce architecture artifacts such as context diagrams, decision records, and reference patterns.
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