ASSOCIATE, DATA ENGINEER

Cresset Capital•Chicago, IL

1d•$85,000 - $110,000•Onsite

About The Position

As part of the technology team, you will develop and maintain Cresset's data platforms in AWS, Matillion, DBT, Databricks, and Snowflake. You will work with systems of record to build and maintain data integrations, supporting our BI & AI Platforms and application integration use cases. Collaborating with senior leadership, business heads, and other data technologists, you will assess data requirements and deliver a robust data platform that drives informed, analytics-driven business decisions.

Requirements

Bachelor's degree in computer science, engineering, or a similar field
1+ years of experience in data engineering for cross-functional business processes involving multiple IT systems.
Experience building API integrations, including learning vendor API documentation, and staying current with updates to the tech stack.
Firsthand experience with Snowflake
Ability to code in SQL
Structured and unstructured data expertise
Strong analytical skills and understanding of technology processes of information and data.
Excellent communication skills and ability to present data analytics to senior leadership.
Strong teamwork and interpersonal skills to collaborate across distinct functions.
Self-motivated and meticulous

Nice To Haves

Experience with Matillion, DBT, Databricks, and AWS
Ability to code in Python or another scripting language
Knowledge of the RIA industry or related financial services
Experience structuring or preparing data for AI/ML pipelines, MCP servers, or similar AI-ready data frameworks.

Responsibilities

Assist in the development of the Cresset Data Warehouse
Assist in developing a data reporting and analytics framework, including industry best practices.
Assist in development and maintenance of BI platforms.
Improve the performance of queries and analysis through data modelling, tuning, and automation.
Collaborate with business partners to prioritize and deliver against data requests and needs.
Acquire and integrate data from multiple sources and systems for analysis.
Present data in a clear and concise manner allowing power users to easily create their own dashboards and reports.
Experience building or contributing to data pipelines that feed AI tools, including Model Context Protocol (MCP) servers or similar AI integration frameworks.
Preparing and transforming datasets for ingestion into LLM-based tools, AI agents, or retrieval-augmented generation (RAG) systems.
Familiarity with data formatting, chunking, schema design, or metadata tagging strategies optimized for AI tool consumption.
Exposure to vector databases, embedding pipelines, or semantic search infrastructure.
Understanding prompt context management and how data structure impacts AI model output quality.