Director, Data Engineer

Cresset Capital•Denver, CO

About The Position

As part of the technology team, you will report to the Managing Director, Head of Investment and Data Technology and will be a lead contributor in building, scaling, and maintaining Cresset’s data platform. A core focus of this role is the design and delivery of end-to-end custodial feed integrations, ingesting and normalizing data from custodians and financial data vendors into our central data platform. You will bring a deep understanding of the RIA and wealth management data landscape and will work closely with investment operations, trading, operations, technology, and business leadership to ensure our data infrastructure supports the firm’s growth and analytical needs. The right candidate is a seasoned data engineer who operates independently, mentors junior team members, and has a track record of owning complex integrations from inception through production.

Requirements

Bachelor’s degree in computer science, engineering, information systems, or a related field.
7 or more years of experience in data engineering, with at least 3 years working in the financial services, RIA, or wealth management industry.
Proven experience building and maintaining custodial feed integrations with major custodians such as Schwab, Fidelity, Pershing, or similar, including file based and API driven data delivery models.
Deep proficiency with all data tools within our stack.
Strong Python skills applied to data engineering workflows including pipeline development, data wrangling, API integrations, and scripting.
Experience with dbt for data transformation, testing, and documentation within a cloud data warehouse environment.
Working knowledge of Matillion or similar ETL orchestration tools for building and managing data pipelines.
Proficiency with AWS services relevant to data engineering including S3, Glue, and Lambda
Experience with Power BI, including building semantic models and datasets that support reliable self-service analytics.
Solid understanding of Git based version control and experience working within CI/CD workflows.
Strong communication skills with the ability to work effectively across technical and non-technical stakeholders.

Nice To Haves

Experience with portfolio reporting platforms such as Addepar and OMS platforms such as Charles River are a plus.

Responsibilities

Design, build, and maintain end to end custodial and financial data feed integrations, including ingestion, normalization, reconciliation, and delivery of data from custodians, third party data vendors, and portfolio accounting systems.
Architect and develop scalable ETL and ELT pipelines using Databricks, Matillion, dbt and Python to move and transform data across the platform.
Help augment the Snowflake data warehouse, including data modeling, schema design, stored procedures, from raw ingestion through reporting ready gold layer tables.
Build and maintain dbt models to transform and document data within Snowflake, ensuring consistency, testability, and lineage across the warehouse.
Collaborate with stakeholders to understand data requirements and translate them into reliable, well-documented data pipelines and data products.
Develop and support Power BI data models and semantic layers that give analysts and business users access to clean, performant data for self-service reporting.
Contribute to and enforce engineering best practices including version control in Git, CI/CD pipeline management and documentation in Confluence and Jira.
Serve as a technical resource and mentor to associate and mid-level engineers on the team, providing guidance on architecture decisions, code quality, and domain knowledge.
Stay current with developments in the financial data and RIA technology ecosystem, proactively identifying opportunities to improve data quality, coverage, and integration efficiency.
Partner with the AI and machine learning team to understand data requirements for AI driven tools and initiatives, ensuring that clean, well structured, and properly governed data is reliably delivered to support model development, feature engineering, and production AI workflows.