Data Engineer

Data Engineer – Enterprise Data & Analytics Engineering Location: Austin, TX or Miami, FL Work Model: Hybrid — 4 days in office Compensation: $150,000 - $170,000 10% About the Company A growing wealth management organization helps high- and ultra-high-net-worth individuals and families manage, preserve, and grow their wealth across generations.

The organization is investing in its enterprise data capabilities to improve reporting, analytics, data quality, and operational decision-making. This is an opportunity to contribute to the data infrastructure supporting a complex, business-critical environment.

About the Role

We're looking for an experienced Data Engineer to build, maintain, and improve enterprise data pipelines, transformations, and models that support analytics, reporting, and data science. This role combines hands-on engineering with operational ownership. You'll help establish best practices across data modeling, ETL/ELT development, data quality, lineage, metadata management, documentation, and monitoring.

You'll also play an important role in ensuring the reliability and integrity of enterprise data, implementing automated validation and reconciliation processes while partnering with Analytics, Operations, and Business teams to deliver dependable data solutions.

The ideal candidate brings strong SQL, Python, and dbt experience, along with a solid understanding of enterprise data platforms and the operational practices needed to keep them reliable.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines, transformations, models, and workflows across the enterprise data layer.
• Build and operate automated data jobs that support analytics, reporting, and data science initiatives.
• Establish engineering best practices for data pipelines, modeling, data quality, lineage, metadata management, documentation, and operational monitoring.
• Develop automated data quality checks, reconciliations, validation processes, and alerting to maintain data integrity.
• Monitor critical datasets for accuracy, completeness, and timeliness across enterprise platforms.
• Identify and resolve data quality issues and improve the reliability of data products.
• Support sprint planning, backlog prioritization, and execution across multiple data initiatives.
• Partner with Analytics, Operations, and Business stakeholders to understand requirements and deliver reliable data solutions.
• Document data processes, transformations, and workflows to support transparency and maintainability.
• Contribute to the ongoing improvement of enterprise data platform architecture and engineering practices.

Must Haves

• 6+ years of experience in Data Engineering, Data Platform Development, or Analytics Engineering.
• Strong proficiency in SQL and Python.
• Hands-on experience with dbt.
• Experience building and maintaining ETL/ELT pipelines and data transformations.
• Strong understanding of data modeling and enterprise data platforms.
• Experience developing data solutions that support enterprise reporting and analytics.
• Knowledge of data quality frameworks, automated validation, reconciliation, and monitoring.
• Understanding of data lineage, metadata management, documentation, and operational best practices.
• Strong analytical, problem-solving, and critical-thinking skills.
• Effective written and verbal communication skills.
• Ability to work independently while collaborating across technical and business teams.

Preferred Background

• Experience in financial services, consulting, or high-growth technology environments.
• Experience supporting enterprise-scale analytics and reporting platforms.
• Exposure to data governance, automated data quality monitoring, and enterprise data operations.
• Strong academic background in Data, Computer Science, or a related discipline.

What Makes Someone Successful Here

The strongest candidates will combine hands-on engineering ability with an understanding of how enterprise data platforms operate in production.

You'll know how to build reliable pipelines and well-structured models, but also how to monitor data quality, investigate discrepancies, document transformations, and prevent recurring issues through automation.

You'll be comfortable balancing development work with operational responsibilities and partnering with stakeholders to ensure that data solutions meet real business needs.

Why Consider This Opportunity?

• Enterprise-level impact:

Build and maintain data capabilities that support business-critical analytics and reporting.

• Technical ownership:

Contribute across pipelines, modeling, transformations, quality assurance, and monitoring.

• Business partnership:

Work closely with Analytics, Operations, and business stakeholders.