Senior Manager, Finance Data Platform

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For over 50 years, AmeriLife has been a leader in the development, marketing and distribution of annuity, life and health insurance solutions for those planning for and living in retirement.

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Job Summary

AmeriLife is seeking a Senior Manager, Finance Data Platform to lead the engineering, delivery, operations, and modernization of our enterprise finance data ecosystem.

Reporting directly to the Vice President of Enterprise Data & AI Platforms, this leader will have responsibility for the Finance Data Platform (FDM) and the broader data capabilities supporting Finance, Accounting, FP&A, Commissions, Banking, General Ledger, financial reporting, attribution, reconciliation, and related financial processes.

This is a hands-on technology leadership role requiring a strong combination of enterprise data platform experience, financial data domain knowledge, engineering depth, and people leadership. The Senior Manager will lead a team of engineers and finance data specialists while working closely with Finance stakeholders to translate complex financial requirements into reliable, scalable, and well-governed data solutions.

A key mandate for this role will be evolving existing FDM capabilities into a modern, enterprise-grade Finance Data Platform built on Databricks, while maintaining the responsiveness required to support fast-moving Finance priorities and business-critical operations.

Job Description

Key Responsibilities

Finance Data Platform Leadership

• Lead the engineering, delivery, operations, and ongoing evolution of the Finance Data Platform.

• Provide day-to-day leadership for engineers, finance data specialists, and partner resources supporting FDM and related finance data capabilities.

• Establish clear priorities across Finance enhancements, strategic initiatives, production issues, reconciliations, data quality, technical debt, and platform modernization.

• Build an operating model that supports rapid Finance-driven changes while maintaining engineering discipline, reliability, governance, and appropriate controls.

• Partner closely with Finance, Accounting, FP&A, Commissions, Data Architecture, Data Engineering, and enterprise technology teams.

• Translate complex Finance requirements and business rules into scalable technical solutions and executable engineering priorities.

• Establish clear accountability across engineering, functional expertise, QA, delivery, and operational support.

Finance & FDM Domain

• Develop deep expertise in the existing FDM ecosystem, including its business processes, data models, transformations, integrations, dependencies, and downstream consumers.

• Lead data capabilities supporting:

• General Ledger and journal entries

• P&L and financial performance

• FP&A, budgeting, planning, and forecasting

• Commissions and compensation

• Banking and cash transactions

• Revenue and expense attribution

• Carrier, producer, policy, contract, and product financial activity

• Entity, company, product, and organizational attribution

• Financial reconciliation and controls

• Management and statutory financial reporting

• Partner with Finance stakeholders to understand calculation logic, mappings, hierarchies, allocations, attribution rules, reconciliation requirements, and reporting dependencies.

• Ensure financial data can be traced and reconciled from source systems through transformation and downstream consumption.

• Support integrations with financial planning, commission, accounting, reporting, and enterprise performance management platforms.

Databricks & Enterprise Data Engineering

• Lead the modernization and migration of Finance data capabilities onto the enterprise   Databricks Lakehouse   platform.

• Design and oversee scalable ingestion, transformation, integration, and publishing patterns for complex financial datasets.

• Apply modern lakehouse and medallion architecture principles across Bronze, Silver, and Gold layers.

• Drive engineering standards including reusable frameworks, metadata-driven pipelines, automated testing, CI/CD, observability, and infrastructure automation.

• Work across databases, APIs, files, SFTP feeds, enterprise applications, and other structured and semi-structured data sources.

• Optimize Databricks workloads for performance, scalability, reliability, and cost.

• Maintain strong SQL and data engineering standards across the team.

• Partner with enterprise Data Architecture and Engineering leadership on data models, integration patterns, architecture standards, and platform strategy.

• Drive the progressive retirement and simplification of legacy FDM components as capabilities transition to the enterprise data platform.

Financial Data Modeling, Quality & Controls

• Ensure data models appropriately represent financial transactions, accounts, entities, products, carriers, producers, policies, contracts, commissions, and organizational hierarchies.

• Establish strong reconciliation capabilities across source systems, FDM, Databricks, General Ledger, financial applications, and downstream reporting.

• Ensure appropriate historical tracking, auditability, lineage, and point-in-time analysis for critical financial data.

• Partner with Data Architecture and Governance teams to establish consistent business definitions, metadata, lineage, ownership, and data quality standards.

• Establish automated controls for completeness, accuracy, timeliness, duplication, referential integrity, and financial reconciliation.

• Ensure appropriate security and access controls for sensitive financial information.

• Drive root-cause analysis and permanent remediation of recurring data quality and reconciliation issues.

Delivery & Operational Excellence

• Lead execution across Finance data projects, enhancements, defects, operational requests, and production issues.

• Establish a delivery model capable of supporting fast-paced Finance requirements that may not always align to traditional sprint-based development cycles.

• Create clear processes for distinguishing and managing enhancements, strategic projects, production incidents, defects, and recurring operational activities.

• Balance immediate Finance priorities with long-term platform modernization and technical sustainability.

• Establish service expectations, prioritization mechanisms, escalation paths, and measurable delivery outcomes.

• Reduce manual processing and operational dependencies through automation and engineering improvements.

• Establish monitoring, alerting, incident management, and production-readiness practices for critical Finance data pipelines.

• Coordinate internal teams and external delivery partners while maintaining clear internal accountability for outcomes.

People Leadership

• Lead, mentor, and develop a high-performing Finance Data Platform team.

• Build a team with the right combination of data engineering, financial domain, functional, and operational expertise.

• Develop team members’ understanding of both the technology and the financial processes supported by the platform.

• Create clear ownership and accountability while enabling engineers and specialists to operate with appropriate autonomy.

• Manage internal employees, contractors, and strategic delivery partners.

• Participate in hiring, workforce planning, performance management, and capability development as the Finance Data Platform organization evolves.

Required Qualifications

• 10+ years of experience across data engineering, enterprise data platforms, data architecture, financial data systems, or related disciplines.

• 3+ years of experience leading data engineering, data platform, or financial data teams.

• Demonstrated experience designing, building, or operating   large-scale ent