Lead Analytics Engineer — Actuarial, Finance & Investments

About the role

The Lead Analytics Engineer is responsible for modeling data to provide clean, accurate, well-documented datasets for use across the organization. They will serve as the subject matter expert for asset and actuarial data models holdings, transactions, security reference, and performance data supporting Finance, Investment Management, and Actuarial stakeholders as investment data migrates from legacy sources onto a modern cloud data platform.

They are expected to review, influence, and support the development of junior engineers, and have a positive influence across the analytics community. They identify new opportunities to create data products that advance company goals.

• Design, build, and maintain scalable data models, curated datasets, and data marts that deliver trusted, business-ready data for Finance, Investment Management, and Actuarial teams.
• Serve as the subject matter expert for investment and actuarial data domains, including holdings, transactions, security master data, valuations, performance, and attribution data.
• Lead the migration and transformation of investment data from legacy platforms to a modern cloud-based data ecosystem, ensuring data quality, consistency, and accessibility.
• Develop and optimize cloud-native data architectures that enable efficient storage, integration, governance, and analysis of critical financial and investment data assets.
• Build robust ETL/ELT pipelines that integrate structured and unstructured data from multiple internal and external sources while ensuring scalability, reliability, and performance.
• Partner closely with Finance, Investment Management, Actuarial, IT, and Analytics teams to understand business requirements and translate them into scalable data solutions.
• Identify opportunities to create new data products, analytical capabilities, and reporting solutions that improve business decision-making and support strategic objectives.
• Apply advanced SQL and Python skills to design data transformations, automate processes, improve data quality, and optimize analytics workflows.
• Establish and promote data engineering best practices, including data modeling standards, testing frameworks, documentation, version control, lineage tracking, and governance processes.
• Mentor and support analytics engineers by providing technical guidance, code reviews, architectural recommendations, and development coaching.
• Optimize data warehouse performance, schema design, access controls, and pipeline orchestration across modern cloud platforms such as Snowflake, Databricks, Redshift, or Big Query.
• Stay current on emerging technologies, tools, and industry trends in data engineering, cloud platforms, investment analytics, and insurance data management.

• You are a strategic data professional who combines deep technical expertise with strong business acumen to deliver scalable analytics solutions that drive meaningful business outcomes.
• You have extensive experience designing data models, data warehouses, and cloud-based data platforms that support complex financial, investment, and actuarial reporting needs.
• You are a collaborative partner who excels at working across technical and business teams, translating complex requirements into practical, high-quality data solutions.
• You are a technical leader and mentor who enjoys developing others, establishing best practices, and elevating the capabilities of the broader analytics and engineering community.
• You are a curious problem solver who thrives in complex environments, proactively identifying opportunities to improve data quality, streamline processes, and unlock new analytical insights.

• Bachelor's degree in computer science, Data Analytics, Information Systems, Mathematics, Finance, Actuarial Science, or a related field, or equivalent experience.
• 8-10 years of experience in analytics engineering, data engineering, business intelligence, or related data-focused roles.
• Advanced expertise in SQL, Python, data modeling, ETL/ELT development, and cloud data architecture.
• Experience with modern cloud data platforms such as Snowflake, Databricks, Big Query, or Amazon Redshift.
• Proficiency…