Senior Data and AI Platform Engineer
New
Remote-first role within the United StatesFull-TimeSenior
SalaryCompetitive base salary ranging from 150,000 to 230,000 USD
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Job Details
- Required Skills
- PythonSQLData engineeringDatabricks
Requirements
- Extensive experience in data engineering, analytics engineering, or data platform engineering roles in production environments
- Strong hands-on expertise with Databricks or similar Lakehouse and cloud data platform technologies
- Proven experience designing scalable data pipelines, governed data models, and enterprise analytics solutions
- Advanced SQL skills and strong proficiency in Python or similar programming languages for production-grade systems
- Deep understanding of data governance concepts including metadata, lineage, access control, and data quality frameworks
- Experience with orchestration, ingestion, transformation, testing, and monitoring of large-scale data systems
- Familiarity with modern AI and analytics concepts such as agentic analytics, semantic layers, or LLM-enabled workflows
- Strong communication skills with the ability to engage both technical and non-technical stakeholders effectively
- Ability to operate as a senior individual contributor driving architecture, influence, and execution without formal management responsibilities
Responsibilities
- Design, build, and maintain scalable data pipelines and platform components supporting analytics, BI, data science, and AI use cases
- Lead the evolution of a Databricks-based Lakehouse platform, defining architecture, governance models, and engineering standards
- Develop and operate ingestion and transformation pipelines that ensure reliable, high-quality data availability across systems
- Enable enterprise-wide analytics and agentic AI capabilities through trusted datasets, semantic models, and governed data access
- Implement and improve platform capabilities such as data lineage, metadata management, observability, and cost/performance monitoring
- Collaborate with cross-functional teams including data science, engineering, product, and business stakeholders to translate requirements into durable data solutions
- Establish reusable frameworks and best practices for data modeling, testing, documentation, and AI-ready data structures
- Provide senior technical leadership through architecture design, code quality standards, mentoring, and engineering decision-making
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