Lead Data Engineer, Master Data Management

We are seeking a Lead Data Engineer, Master Data Management to help shape the future of data at Clearstead. This is an opportunity to join our Data & AI team at a foundational stage and play a hands-on role in building the data pipelines, integrations, governance practices, and master data capabilities that bring our enterprise data together.

You’ll work at the intersection of data engineering, master data management, governance, and AI, helping ensure that critical business data is accurate, consistent, secure, and accessible to the people and applications that rely on it. This role is designed for someone who enjoys building solutions while also helping define the standards and practices behind a strong enterprise data environment.

Key Responsibilities

Data Engineering & Integration

• Design and implement real-time and batch data processing solutions that support modeling, analytics, and reporting.

• Develop and maintain scalable data pipelines and API integrations as data volumes and complexity continue to grow.

• Build and manage complex datasets that meet business and technical requirements.

• Develop database applications and data integration layers using database programming languages, ETL tools, and scripting languages.

• Establish and promote data engineering standards, documentation, coding practices, and technical best practices.

• Apply security standards and controls to help protect enterprise data and code.

• Connect data across cloud, multicloud, and on-premises environments.

• Research and recommend enhancements to database design, development practices, tools, and platform capabilities.

• Partner with data scientists and business teams to support modeling, reporting, data processing, and visualization needs.

• Troubleshoot data design, engineering, and integration issues and develop effective solutions.

Data Governance & Master Data Management

• Develop and implement enterprise data governance policies, standards, and ownership frameworks.

• Establish trusted, unified records for clients, households, legal entities, and accounts.

• Lead data matching, entity resolution, duplicate identification, and stewardship processes.

• Establish business definitions, data dictionaries, and certification standards for critical datasets.

• Partner across the organization to strengthen data ownership and promote consistent data management practices.

Data Quality & Platform Oversight

• Design and implement automated data quality checks, reconciliation processes, and remediation procedures.

• Co-own the Databricks platform with the Databricks Solution Architect, including Unity Catalog and data access controls.

• Provide second-line support for the Databricks platform.

• Monitor data integrity and resolve quality issues across enterprise systems.

• Support data integration and standardization as new firms and systems are incorporated into the organization.

Business Partnership & Leadership

• Establish and lead cross-functional data ownership and governance forums.

• Work with business leaders to standardize data definitions and resolve inconsistencies.

• Communicate data quality findings, risks, and opportunities for improvement to stakeholders.

• Drive accountability for enterprise data quality and governance standards.

• Chair the firm’s quarterly review of AI agents.

• Collaborate across wealth management, investments, operations, finance, compliance, and technology.