We are partnering with an advanced AI research organization to hire a Senior Data / Analytics Engineer who will play a critical role in building and scaling modern, Snowflake-native data and machine learning pipelines. This position sits at the intersection of analytics engineering and applied machine learning, with a strong emphasis on DBT-driven transformations and Snowflake Cortex CLIbased AI/ML workflows.

In this role, you will help design and operationalize end-to-end data and ML pipelines directly within Snowflake, leveraging emerging Cortex capabilities while ensuring analytics models remain reliable, testable, and production-ready. You will collaborate closely with data engineering, analytics, and machine learning teams to prototype new AI-driven use cases, productionize them, and establish best practices for long-term scalability and governance. This is a high-impact opportunity to shape how AI and analytics coexist within a fully cloud-native data stack.

Key Responsibilities :

You will be responsible for designing, building, and maintaining robust DBT projects that follow modular data modeling principles, semantic consistency, and analytics engineering best practices. This includes developing reusable DBT models, macros, tests, and documentation to support trusted, scalable analytics.

A core part of the role involves integrating DBT workflows with Snowflake Cortex CLI. You will enable and support Snowflake-native feature engineering pipelines, model training and inference workflows, and automated orchestration of AI and analytics pipelines. You will also help define approaches for monitoring, evaluating, and iterating on Cortex-powered machine learning models directly within Snowflake.

You will work closely with data scientists and ML engineers to translate experimental Cortex workloads into stable, production-grade pipelines. This includes aligning on feature definitions, data contracts, and lifecycle management for models and predictions.

You will establish and document best practices for DBTCortex architecture, including project structure, dependency management, and deployment strategies. As part of this, you will help guide teams on how to balance experimentation with reliability in a shared Snowflake environment.

You will design and optimize CI/CD pipelines for DBT using tools such as GitHub Actions, GitLab CI, or Azure DevOps, ensuring automated testing, validation, and safe deployments across environments. Performance and cost optimization will also be a key focus, including tuning Snowflake warehouses, queries, tasks, and materialized views to achieve efficient, scalable workloads.

Additionally, you will troubleshoot issues across DBT artifacts, Snowflake objects, lineage, and data quality layers. You will provide leadership on DBT governance, testing frameworks, documentation standards, and long-term maintainability of analytics and ML pipelines.

Required Qualifications :

- 3+ years of hands-on experience with DBT Core or DBT Cloud, including building models, macros, packages, tests, and managing deployments.

- Strong expertise with Snowflake, including warehouses, tasks, streams, materialized views, and performance optimization.

- Hands-on experience with Snowflake Cortex CLI, or a demonstrated ability to learn and apply it quickly in production environments.

- Advanced SQL skills, with working knowledge of Python for scripting, automation, and DBT-related workflows.

- Experience integrating DBT with orchestration tools such as Airflow, Dagster, Prefect, or similar platforms.

- Solid understanding of modern data engineering and ELT patterns, version-controlled analytics development, and collaborative data workflows.

Nice-to-Have Skills

- Experience operationalizing machine learning workflows entirely within Snowflake.

- Familiarity with Snowpark and Python-based UDFs or UDTFs.

- Experience building semantic layers using DBT metrics or similar abstractions.

- Knowledge of MLOps and DataOps best practices, including model monitoring and lifecycle management.

- Exposure to LLM-driven workflows, vector search, and unstructured data pipelines within modern data platforms.