Senior AI Analytics Engineer

About the Role As a Senior AI Analytics Engineer in our Data Team, you will bridge the gap between Data Engineering, Tech, and Product. You will play a pivotal role in operationalizing AI/LLM models, building scalable data transformation pipelines, standardizing data models, and establishing best practices. In addition to core analytics engineering, you will occasionally act as a technical Business Analyst on select data initiatives—partnering with business teams to translate ambiguous operational needs into clear technical specs and data requirements.

Data & AI Architecture: Design, build, and maintain production-grade data models to bridge raw data streams with downstream applications and BI tools. Using LLMs and AI tools to generate data documentation, write SQL/Python transformations, convert warehouse dialects, and diagnose pipeline error logs. Analytics Engineering: Standardize data modeling practices and build robust, reliable, scalable semantic layers that feed clean, trustworthy data to downstream analytics and reporting. Business & Requirements Analysis: Occasionally lead scoping discussions with product, tech and finance stakeholders to translate business requirements into functional specs, user stories, and data mapping documents. Business Logic Translation: Reconciling data definitions across departments, a critical human-led task that AI cannot fully automate Data Quality & Governance: Establish automated testing, monitoring, and data quality frameworks to guarantee financial-grade reliability. Performance Optimization: Optimize query performance, warehouse costs, and pipeline orchestration.

• Experience: 5+ years of experience in Data Engineering, Analytics Engineering, or Data Science, with at least 2 years in an AI environment. • Technical Mastery: Advanced SQL capabilities. Hands-on expertise with modern data transformation and orchestration frameworks ( dbt , Airflow , Prefect , Dagster ). Deep experience with cloud data warehouses ( Snowflake , Databricks , BigQuery ). • AI Technical Stack: Understanding of feature engineering, AI agentic workflow lifecycles, and evaluation metrics. Experience with deploying AI agentic workflows in production environments. • Business Analysis Skills: Demonstrated ability to occasionally gather business requirements, define key metrics, and translate stakeholder needs into structured data assets. • Software Engineering Principles: Strong experience with version control ( Git ), CI/CD practices for data pipelines, and production code testing.