Senior AI Analytics Engineer

About the role

In this role, you will be responsible for designing and maintaining data models that connect raw data streams to applications and BI tools. You will also standardize data modeling practices and ensure the integrity of data for analytics and reporting.

You will work closely with stakeholders from product, tech, and finance to translate business requirements into functional specifications and data mappings. Additionally, you will establish frameworks for data quality and governance while optimizing performance and costs.

Skills that matter here

• SQL: This role requires advanced SQL skills for data manipulation and querying.
• Python: Python will be used for data processing and automation tasks.
• dbt: Knowledge of dbt is essential for transforming and modeling data.
• Airflow: You will utilize Airflow for orchestrating data workflows.
• Snowflake: Experience with Snowflake is necessary for managing cloud data warehouses.
• Git: Version control using Git will be important for maintaining code integrity.

Who this role suits

• A professional with over 5 years of experience in data engineering or analytics, including 2 years in AI.
• Someone who enjoys translating complex business requirements into structured data solutions.
• A detail-oriented individual who values data quality and governance.
• A team player comfortable working in a hybrid environment and collaborating with diverse stakeholders.

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.
• 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.