Data Engineer

This is a hands-on data engineering contract focused on bridging the gap between heavy enterprise analytics and modern AI infrastructure within wealth management. If you want to move beyond standard ETL pipelines and start building the data plumbing for vector search and RAG applications, this 12-month gig gives you direct access to that modern stack.

What You'll Actually Be Doing

You'll spend the vast majority of your time writing clean SQL and Python to build scalable data pipelines, split between traditional warehousing tasks and modern AI plumbing. Day-to-day involves wrangling large datasets in Snowflake, orchestrating complex workflows with Airflow, and transforming data using dbt, all while supporting emerging machine learning systems.

The Core Tech Stack

Your bread and butter here will be Snowflake, advanced SQL, Python, dbt, and Airflow. The team really cares about your ability to write maintainable transformation logic in dbt and manage scheduling efficiently, while bringing enough Python muscle to handle custom data ingestion and integration with AI/ML systems.

Interview Expectations

Expect to be grilled on how you design idempotent data pipelines in Airflow and handle late-arriving data in Snowflake. The interviewer is secretly trying to figure out if you'll write spaghetti code that breaks in production or if you actually understand how to build resilient, self-healing data architectures.

Application Advice

Make sure your resume puts Snowflake, dbt, and Python right at the top, and explicitly highlight any experience you have with workflow orchestration using Airflow. If you have touched vector databases, LLMs, or cloud-native data platforms, weave those in immediately to easily clear the automated screening.