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.
Key Responsibilities
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.
Requirements & Qualifications
• 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.
Summary
Build production data models, semantic layers, and AI-powered data pipelines while occasionally translating business requirements into technical specs. Requires 5+ years in data engineering or analytics, advanced SQL, hands-on dbt/Airflow/Prefect/Dagster experience, deep cloud warehouse experience, and production deployment of AI agentic workflows.
Responsibilities
Design and maintain production data models and semantic layers; use LLMs and AI tools for SQL/Python transformations, documentation, dialect conversion, and log diagnosis; translate business requirements into specs and data mappings; reconcile data definitions; establish data quality and governance frameworks; optimize query performance, warehouse costs, and pipeline orchestration.
Qualifications
5+ years in Data Engineering, Analytics Engineering, or Data Science, including 2+ years in an AI environment; advanced SQL; hands-on expertise with dbt, Airflow, Prefect, or Dagster; deep experience with Snowflake, Databricks, or BigQuery; Git, CI/CD, and production code testing; experience deploying AI agentic workflows in production.
Experience requirements
5+ years of experience in Data Engineering, Analytics Engineering, or Data Science, with at least 2 years in an AI environment.