Analytics Architect/Lead with AI Experience

• Build, enhance, and maintain production data models using dbt
• Extend existing dimensional models while following established engineering patterns
• Develop transformations across structured and semi-structured datasets
• Apply medallion architecture principles across Silver and Gold data layers
• Optimize models for scalability, maintainability, and performance

Production Pipeline Support

• Contribute to the operation and ongoing enhancement of production data pipelines running on Databricks
• Monitor scheduled workflows and investigate pipeline failures during business hours
• Troubleshoot production issues across data models, orchestration, and upstream dependencies
• Coordinate with upstream and downstream teams to resolve data delivery issues
• Restore production workflows and communicate status during operational incidents
• Escalate larger architectural or platform issues when appropriate

SQL & Data Engineering

• Write and optimize complex SQL across large-scale datasets
• Develop and maintain dbt models that support reporting and downstream systems
• Contribute to PySpark-based data processing where appropriate
• Improve query performance and warehouse efficiency

Problem Solving & Analysis

• Investigate data quality issues and implement appropriate fixes
• Perform exploratory analysis to support engineering and business needs
• Translate loosely defined requirements into practical data solutions
• Independently diagnose issues across unfamiliar datasets and existing codebases

Collaboration

• Work closely with analytics engineers, data engineers, product teams, and business stakeholders
• Participate in code reviews and team design discussions
• Collaborate with upstream and downstream engineering teams when dependencies or production issues arise
• Communicate progress, risks, and blockers clearly throughout assigned work

Quality & Standards

• Follow established engineering standards for development, testing, deployment, and code review
• Implement testing and validation for new and modified dbt models
• Update documentation as needed to reflect implemented changes
• Produce maintainable, production-ready code that aligns with existing team practices

How We Work

Ownership & Execution

• Work is prioritized through a structured backlog and assigned based on team priorities and capacity
• Team members take ownership of individual features, operational issues, and production incidents while collaborating closely across the broader engineering team
• Engineers contribute across the full development lifecycle from implementation through deployment, production support, and maintenance

Engineering Standards

• Development follows established patterns for modeling, testing, deployment, and code review
• CI/CD workflows are used to promote consistency and code quality across environments
• Development and production environments are clearly separated within our Databricks platform
• Engineers are expected to work within existing architecture and engineering standards while delivering high-quality solutions

AI-Assisted Development

• We incorporate AI tools such as GitHub Copilot into our engineering workflow to improve productivity and accelerate development
• AI is used within established engineering standards and review processes
• Candidates are not expected to be AI experts but should be comfortable using modern developer tooling as part of day-to-day engineering work

• 5+ years of experience as an Analytics Engineer, Data Engineer, or Data Analyst with a strong engineering focus
• Expert-level SQL skills, including complex query development, optimization, and data modeling
• Extensive hands-on experience developing and maintaining production dbt projects
• Experience working with cloud-based data platforms (AWS preferred)
• Experience with modern data platforms such as Databricks, Unity Catalog, Snowflake, or similar technologies
• Experience supporting production data pipelines and troubleshooting operational issues
• Ability to independently investigate unfamiliar systems and drive issues to resolution with minimal supervision
• Strong communication and collaboration skills when working across technical teams

Nice-to-Have Qualifications

• Experience with Databricks
• Experience with PySpark or other distributed data processing frameworks
• Experience with CI/CD workflows for analytics engineering (GitHub Actions or similar)
• Familiarity with dimensional modeling and medallion architecture
• Experience supporting business-critical reporting environments, * Experienced analytics engineer who can become productive quickly within an existing codebase
• Someone who enjoys solving complex data problems with an engineering mindset
• A self-directed engineer who is comfortable investigating production issues and driving them to resolution
• A pragmatic developer who values maintainable, reliable, and well-tested solutions
• A collaborative teammate who communicates clearly and works effectively across engineering and business partners
• Someone who can confidently contribute to an established analytics platform while maintaining high engineering standards