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The Data Governance Engineer advances data governance initiatives, focuses on metadata cataloging, lineage harvesting, and governance workflows, and collaborates on data quality and compliance.

Design and execute enterprise data architecture strategy, including logical, physical, operational, and analytical data models. Establish data management, governance, quality, privacy, and security standards while supporting scalable cloud data platforms. Collaborate with product owners, architects, engineers, data scientists, and business teams to translate requirements into data solutions. Lead modernization, legacy simplification, performance optimization, integration, streaming, and organizational change initiatives across insurance and financial services data environments.

Lead development and operationalization of enterprise data risk frameworks, KRIs/KPIs, and risk registers. Produce dashboards and reporting, track remediation, partner with data, technology, risk, and compliance teams, and support data protection, governance, and adoption activities across cloud data environments.

The Senior Associate will leverage financial planning and risk management expertise to analyze models, perform investment performance analysis, and enhance strategic planning by managing portfolio risks. Requires a Master's degree and relevant experience in quantitative financial modeling, with a focus on optimizing data processes.

Lead a small second-line team providing independent oversight of enterprise data governance. Assess governance frameworks, data ownership, quality controls, risk identification, and regulatory alignment. Deliver actionable insights to senior management, partner with first-line teams to improve governance maturity, and validate controls and issue management practices across the firm.

The Data Governance and Stewardship Lead will develop and implement a data governance framework, ensuring data quality and compliance, and collaborating with business and technology teams.

Lead design and delivery of scalable analytics engineering solutions: build dbt transformation pipelines on Databricks/Postgres/BigQuery, develop tested dimensional models and feature tables, implement data quality and governance, improve SDLC and CI/CD, create reusable dbt patterns and semantic metrics, and engage stakeholders to translate requirements and mentor junior engineers.

Support data management and analytics for investment strategies by ensuring accuracy and integrity of portfolio data, performing reconciliations, automating end-to-end workflows, collaborating with technology and business stakeholders, and delivering recurring analytics and reports for sales, marketing, risk, and investment teams.

Participate in projects to improve and standardize technology operations. Collaborate with cross-functional teams to redesign processes, document improvements, create dashboards and reports, analyze data trends, and identify automation opportunities to increase operational effectiveness and adoption of shared tools and practices.

Summer internship focused on building scalable data architectures and pipelines, automating data processes, improving data quality frameworks, and supporting cloud infrastructure. Work includes AWS automation, data warehousing, workflow optimization, platform integration, and operationalizing tools to process structured and unstructured data while leveraging analytics, AI/ML, and standardized pipelines.