Director, Data Solutions

Ascensus•Needham, MA

About The Position

The Director, Data Solutions leads the data foundation for Ascensus Data & Analytics, including architecture, engineering, integration, and quality work that supports enterprise reporting, client-experience analytics, and predictive modeling. The role connects disparate source systems into an enterprise intelligence layer so teams can draw insights across the organization and build analytics on trusted data. Reporting to the Senior Vice President, Data & Analytics, the Director leads a team spanning data engineering, data product management, and business analysis. The role partners with Technology on data priorities, pipeline delivery, and platform standards, initially focused on the Retirement business line, and works with analytics leaders and business stakeholders to prioritize delivery. The Director defines the shared data environment’s architecture and technical standards; builds integrated, reusable enterprise data sets; automates recurring reporting and data preparation; and establishes data quality, governance, and consistent enterprise definitions. The role enables predictive, propensity, and emerging agentic workflows by preparing and governing data in partnership with data science and analytics. It also coordinates with Technology on priorities, platform upgrades, and support, develops the team, and communicates technical constraints, trade-offs, status, dependencies, and risks to business and executive stakeholders. The position is responsible for protecting confidential Ascensus data from unauthorized access, improper transmission, or unapproved disclosure. Ascensus expects employees to embrace emerging technologies and practices that advance its mission and client commitments and to demonstrate its Core Values: People Matter, Quality First, and Integrity Always®.

Requirements

10 or more years of experience in financial services in data engineering, data management, business intelligence, or systems analysis.
At least three years managing a data or reporting team.
Experience owning a recurring, deadline-driven data production cycle, such as regulatory reporting, capital planning or stress testing submissions, financial close, or executive reporting, and streamlining it over successive cycles.
Strong SQL skills, including optimization of complex queries and tuning of stored procedures.
Proficiency in ETL/ELT and pipeline design, dimensional and semantic modeling, and modern cloud data platforms.
Experience with layered data architectures, data lineage, and data quality tooling.
Experience designing, developing, testing, and administering internal applications and reporting tools, including replacing vendor products when an internal solution better serves the business.
Experience with software development lifecycle methodologies such as Agile Scrum or Kanban, using tools such as Azure DevOps or Jira for requirements and delivery management.
Practical understanding of upstream data requirements for predictive, propensity, and generative AI use cases, including structure, granularity, freshness, lineage, and governance.
Ability to connect data investments to commercial outcomes and explain the value and trade-offs of foundational work to non-technical leaders.
Proven ability to build working relationships across Technology, Operations, Product, and business teams, and exercise good judgment independently.
Bachelor’s degree in Computer Science, Information Systems, Engineering, Finance, or a related field, or equivalent work experience.
For virtual remote positions, an uninterrupted virtual workspace, family care in place during business hours, and an internet connection meeting the stated speed requirements are expected.

Nice To Haves

Working familiarity with Ascensus’ Microsoft data stack: SQL Server, SSIS, SSAS, and Power BI semantic models.
Experience in retirement, wealth management, asset management, banking, or fintech.
An advanced degree or relevant data platform certifications.

Responsibilities

Define the target architecture for the shared data environment, including its layered (medallion) structure, semantic models, and reporting layers, in partnership with Technology and enterprise architecture.
Set and uphold standards for pipeline design, source control, testing, deployment, and documentation.
Build integrated data sets that connect sources across the enterprise, including data related to client interactions, book of business, customer experience, behavioral trends, products, and partners.
Deliver a shared client-interaction data source covering digital activity, workflows, interactions, and sentiment, as well as an advisor data pipeline connecting core platform data with CRM sales activity.
Automate manual, repetitive reporting and data preparation; track cycle time and manual touchpoints and reduce them.
Maintain a prioritized data roadmap with the data product owner, aligned to analytics and business priorities.
Build, document, and assign ownership for reusable data sets to reduce rebuilding across projects.
Establish a single source of truth for enterprise reporting and client insight, maintain consistent enterprise data definitions, run data quality audits, and identify foundational-system gaps that hinder enterprise reporting or cross-sell insight.
Prepare and govern data for predictive and propensity modeling and emerging agentic workflows in partnership with data science and analytics.
Coordinate with Technology on data priorities, platform upgrades, and the ongoing support model; represent Data & Analytics requirements in architectural reviews and influence scalable, consistent decisions.
Lead, coach, and develop a team spanning data engineering, data product management, and business analysis; set delivery and engineering expectations, mentor through code and design reviews, and build team capacity as the function grows.
Explain technical constraints and trade-offs in business terms and communicate status, dependencies, and risks early to analytics leaders, business stakeholders, and executives.
Protect, secure, and properly handle confidential Ascensus data.
Adopt and use emerging technologies, platforms, and practices, including AI and automation, to support transformation and client commitments.