Senior AI Analytics Engineer

About the role

In this role, you will be responsible for designing and maintaining production-grade data models that connect raw data streams with business intelligence tools. Your work will involve using AI tools to enhance data documentation and ensure the reliability of data pipelines.

You will also work closely with stakeholders from various departments to translate business requirements into functional specifications. This position requires a strong focus on data quality and governance, as well as optimizing query performance and costs associated with data warehousing.

The salary range for this position is between $112,000 and $188,000, reflecting the seniority and expertise required.

Skills that matter here

• SQL: Advanced SQL capabilities are essential for transforming and optimizing data.
• Python: Hands-on expertise in Python is needed for data transformations and AI workflows.
• dbt: Experience with dbt is required for modern data transformation practices.
• Airflow: Knowledge of Airflow is important for orchestrating data pipelines.
• Snowflake: Deep experience with Snowflake is necessary for managing cloud data warehouses.
• Git: Strong experience with Git is required for version control in data engineering.

Who this role suits

• A candidate with over 5 years in data engineering or analytics, including 2 years specifically in AI environments.
• Someone who is comfortable leading discussions with stakeholders to gather business requirements.
• An individual who values data quality and governance and has a strong understanding of software engineering principles.
• A professional who enjoys optimizing performance and is familiar with modern data orchestration frameworks.

From the employer

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.