Job description
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.
We may use artificial intelligence tools to analyze the content of your Resume/CV against the specific requirements for the position. The purpose is to support our recruitment team in reviewing applications more effectively. These tools assist our recruitment team in their evaluation of your application by providing recommendations, but they do not replace human judgment. Final hiring decisions are ultimately made by humans who consider the insights generated by the tools along with other relevant information. If you would like more details about how your personal information is processed, please contact us.