Senior Data & AI Platforms Engineer

Guild is a company that believes in equal opportunity and talent development. They are seeking a Senior Data & AI Platforms Engineer to design, build, and scale the data infrastructure that supports analytics and product innovation across the organization.

Responsibilities:

• Build and maintain reliable, scalable data pipelines that deliver trusted data to analysts, stakeholders, and product teams on time and without manual intervention
• Deliver accurate, well-modeled data marts and models that directly enable self-serve analytics and confident business decision-making
• Expand the data platform's scope to support production engineering use cases from the warehouse, establishing patterns and prototypes that the broader organization can adopt
• Architect, develop, and maintain data pipelines using Fivetran, dbt, S3, Glue, orchestrators, and custom Python jobs to keep data flowing reliably across the organization
• Manage and optimize data models in dbt, ensuring they are scalable, accurate, and aligned to business use cases
• Integrate and transform data from sources including AuroraDB, Snowflake, Postgres, MySQL, and DynamoDB
• Collaborate with analytics and commercial teams to design models and marts in visualization tools that empower decision-making
• Uphold data quality through testing, validation, and observability best practices
• Provide enterprise-wide guidance and prototyping on key data initiatives, especially as the platform expands to support production engineering from the warehouse
• Mentor and upskill team members while contributing to evolving data engineering standards and processes

Requirements:

• Build and maintain reliable, scalable data pipelines that deliver trusted data to analysts, stakeholders, and product teams on time and without manual intervention
• Deliver accurate, well-modeled data marts and models that directly enable self-serve analytics and confident business decision-making
• Expand the data platform's scope to support production engineering use cases from the warehouse, establishing patterns and prototypes that the broader organization can adopt
• Architect, develop, and maintain data pipelines using Fivetran, dbt, S3, Glue, orchestrators, and custom Python jobs to keep data flowing reliably across the organization
• Manage and optimize data models in dbt, ensuring they are scalable, accurate, and aligned to business use cases
• Integrate and transform data from sources including AuroraDB, Snowflake, Postgres, MySQL, and DynamoDB
• Collaborate with analytics and commercial teams to design models and marts in visualization tools that empower decision-making
• Uphold data quality through testing, validation, and observability best practices
• Provide enterprise-wide guidance and prototyping on key data initiatives, especially as the platform expands to support production engineering from the warehouse
• Mentor and upskill team members while contributing to evolving data engineering standards and processes
• Built and maintained production-grade data pipelines using tools like Snowflake, dbt, and Fivetran in a fast-paced, high-growth environment
• Designed scalable data models and warehousing solutions that connected technical infrastructure with commercial or product outcomes
• Worked cross-functionally with analytics, product, or business teams to translate data needs into reliable, self-serve solutions
• Navigated complex source systems (relational databases, NoSQL, APIs) and built integrations that handled messy, real-world data at scale
• Expert-level SQL (window functions, CTEs, performance tuning) and Python (data pipelines, API integrations, automation)
• Hands-on experience with Snowflake, dbt, and Fivetran, plus comfort navigating Unix systems, Terraform, and Git
• Deep familiarity with relational databases (Postgres, MySQL, AuroraDB) and NoSQL systems such as DynamoDB
• Strong understanding of data modeling, warehousing best practices, and ETL/ELT frameworks
• Experience with CDC (Change Data Capture) patterns, data governance, or CI/CD practices in a data engineering context
• Proven ability to work cross-functionally and connect technical design with commercial objectives