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.
Build and maintain trusted RevOps data models and pipelines, translate complex data into business insights, and apply AI and predictive analytics to support Sales, Marketing, and Customer Experience decisions.
Build and maintain analytics models and pipelines for Revenue Operations, translating complex data into business insights while applying AI and machine learning to predictive analytics and stakeholder reporting.
Build and maintain analytics models, pipelines, and Tableau reporting for Revenue Operations, translating complex data into business insights and applying AI and machine learning to predictive use cases.
Build and maintain analytics models and pipelines for Revenue Operations, translating complex data into business insights while applying AI and machine learning to predictive analytics and data discovery.
Own the dbt transformation and Looker semantic layers, turning raw data into trusted models, consistent business metrics, and self-serve reporting that helps teams make better decisions.
