Senior AI Analytics Engineer
About the Role
As a Senior AI Analytics Engineer in our Data Team, you will serve as a key link between Data Engineering, Technology, and Product teams. Your primary responsibilities will involve operationalizing AI/LLM models, developing scalable data transformation pipelines, standardizing data models, and implementing best practices.
In addition to your core duties in analytics engineering, you will also be involved as a technical Business Analyst on certain data projects, collaborating with business units to translate operational needs into clear technical requirements.
Key Responsibilities
Data & AI Architecture: Develop and manage production-quality data models to connect raw data streams with downstream tools and BI, utilizing AI tools for data documentation, writing SQL/Python transformations, handling warehouse dialect conversions, and troubleshooting pipeline errors.
Analytics Engineering: Establish standardized data modeling approaches and build strong semantic layers to deliver clean, reliable data for analytics and reporting downstream.
Business & Requirements Analysis: Lead scoping discussions with various stakeholders to convert business requirements into functional specifications, user stories, and data mapping documents.
Business Logic Translation: Manage the alignment of data definitions across departments, a critical task that requires human intervention.
Data Quality & Governance: Implement automated testing, monitoring, and governance frameworks to ensure high reliability.
Performance Optimization: Enhance query performance, reduce warehouse costs, and streamline pipeline orchestration.
Requirements & Qualifications
Experience: Minimum of 5 years in Data Engineering, Analytics Engineering, or Data Science, with at least 2 years in an AI-focused environment.
Technical Mastery: Advanced skills in SQL, proficiency with modern data transformation and orchestration tools such as dbt, Airflow, Prefect, and Dagster, and deep knowledge of cloud data warehouses like Snowflake, Databricks, and BigQuery.
AI Technical Stack: Understanding of feature engineering, AI workflow lifecycles, and evaluation metrics, along with experience deploying AI workflows in production settings.
Business Analysis Skills: Ability to collect business requirements, define key metrics, and translate stakeholder needs into structured data assets as needed.
Software Engineering Principles: Strong background in version control using Git, CI/CD practices for data pipelines, and code testing for production environments.
We may utilize artificial intelligence tools for applicant screening, but final hires are based on human evaluation in conjunction with tool insights.

