Data Engineer

Moneta Group Investment Advisors LLC•St. Louis, MO

22h

About The Position

The Data Systems Engineer serves as the critical link between IT infrastructure and day-to-day Client Team data needs. This role is equally responsible for proactive data stewardship — building, maintaining, and improving data systems — and responsive technical support when issues arise. The position sits within IT but is primarily oriented toward serving the Client Team, supporting vendor-managed data environments, internal SQL databases, and the tooling ecosystem that Client Team depends on.

Requirements

Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent professional experience.
2–4 years of experience in data engineering or a related technical role.
Proficiency in SQL and Python; experience with Microsoft Fabric ETL tools preferred.
Hands-on experience with SQL Server, Snowflake, and MuleSoft.
Familiarity with Power BI and Power Apps for analytics and internal tooling.
Understanding of data modeling (dimensional and normalized), data warehousing, and pipeline orchestration.
Strong written and verbal communication skills with the ability to collaborate across technical and non-technical teams.
Must be authorized to work in the United States.

Nice To Haves

Experience with Palantir a plus.

Responsibilities

Maintain the ongoing accuracy and completeness of vendor-specific databases, including regular updates and integrity reviews.
Support data ingestion efforts and ensure ongoing data quality within SQL environments.
Execute bulk data changes in underlying systems on an agreed-upon cadence coordinated with client team leadership, owning the scheduling, communication, and execution of that cadence.
Establish and maintain a QA or staging process prior to any bulk changes going live, including documentation of testing steps and sign-off requirements.
Perform performance tuning and query optimization across shared database resources.
Identify and resolve upstream data issues in coordination with relevant stakeholders, including portfolio data discrepancies and settlement timing inconsistencies.
Serve as the primary point of contact for corrections and escalations related to vendor-owned source data.
Conduct periodic integrity reviews of vendor data and proactively build automated or semi-automated checks to catch issues before they impact operations.
Maintain working knowledge of vendor data structures and flag anomalies to internal teams and vendor contacts in a timely manner.
Manage SQL permissions, database creation, installation, and authentication across relevant environments.
Own the technical execution of API layer setup and maintenance, including onboarding new data sources and managing data migrations.
Coordinate with IT for installation and configuration of approved tools and software.
Serve as the escalation point for access and permission issues requiring admin-level resolution.
Review internally developed tools, scripts, and workflows for technical soundness, security, and data integrity prior to deployment.
Serve as a knowledgeable resource for project planning and architecture decisions, including review of AI-assisted build plans and code outputs.
Contribute to the development and documentation of internal technical standards and best practices.
Serve as the first technical point of contact for data-related incidents, including vendor API failures, failed bulk operations, permission outages, or data corruption events.
Acknowledge data-related incidents within the same business day and pursue resolution as quickly as circumstances allow, coordinating with IT for infrastructure-level escalations.
Maintain clear documentation of all bulk changes, data corrections, vendor escalations, and schema modifications.
Keep a living record of database structures, access permissions, and integration configurations.
Ensure changes are traceable and reversible where possible.
Apply sound data governance principles across all work, including consistent naming conventions, controlled access and permission hygiene, change management practices that protect data integrity, and appropriate handling of data when working with AI-assisted tools.