Senior AI Analytics Engineer

About the role

As a Senior AI Analytics Engineer at Crypto, you will be responsible for designing and maintaining production-grade data models that connect raw data with downstream applications and business intelligence tools. This role requires a strong foundation in SQL and Python, along with experience in modern data transformation frameworks and cloud data warehouses.

You will also standardize data modeling practices, ensuring that clean and trustworthy data feeds into analytics and reporting systems. Your work will involve collaborating with various stakeholders to translate business requirements into functional specifications while establishing automated testing and monitoring frameworks to maintain data quality.

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

Skills that matter here

• SQL: This role requires advanced SQL capabilities for data transformations and performance optimization.
• Python: Python is essential for writing transformations and managing data workflows.
• dbt: Experience with dbt is needed for modern data transformation processes.
• Airflow: Airflow is used for orchestrating data pipelines in this position.
• Snowflake: Deep experience with Snowflake is necessary for working with cloud data warehouses.
• Git: Strong experience with Git is required for version control in data pipeline development.

Who this role suits

• Candidates should have a minimum of 5 years of experience in data engineering, analytics engineering, or data science.
• A collaborative mindset is essential, as you will work with various stakeholders to gather requirements.
• Individuals who thrive in this role are comfortable with both technical and business analysis tasks.
• Strong problem-solving skills and an ability to optimize performance in data pipelines are crucial.

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.