Director, Data Solutions

Ascensus; United States; Remote; USD 180,000 - 230,000; Full time

Job summary

Ascensus is seeking a Director of Data Solutions to lead the data foundation for Ascensus Data & Analytics. You will architect, engineer, integrate and govern data used for enterprise reporting, client analytics and predictive modeling.

The role reports to the SVP and leads a team spanning data engineering, data product management and business analysis. Collaboration with Technology and business stakeholders is essential to establish priority and scale data solutions.

Qualifications

• 10+ years in financial services data engineering, management or BI, incl. 3+ years leading a team.
• Experience owning recurring, deadline-driven data production cycles with process improvements.
• Strong SQL and ETL/ELT, modern cloud platforms, data lineage and quality tooling.
• Familiarity with Microsoft data stack (SQL Server, SSIS/SSAS, Power BI).
• Ability to design, test and deploy internal reporting tools and apps.
• Knowledge of Agile/Scrum or Kanban and delivery tools (Azure DevOps, Jira).
• Understanding of predictive/propensity/generative AI and data requirements.
• Industry experience in retirement, wealth or asset management or fintech preferred.
• Bachelor’s degree or equivalent; advanced degree or certifications preferred.

Responsibilities

• Define target data architecture, standards for pipelines, testing and documentation.
• Build integrated data sets and trusted source of truth across the enterprise.
• Automate reporting and data prep to reduce manual effort and cycle time.
• Maintain a prioritized data roadmap aligned with analytics priorities.
• Govern data quality, definitions and governance across lines of business.
• Structure data for predictive analytics and AI-enabled workflows.
• Coordinate with Technology on priorities, platform upgrades and support.
• Lead and develop a team of data engineers, product managers and analysts.
• Explain constraints and trade-offs clearly to executives and stakeholders.

Skills

Leadership

Data Architecture

SQL & Query Tuning

ETL/ELT & Pipeline Design

Cloud Data Platforms

Agile / Delivery

Stakeholder Communication

Collaboration Across Teams

AI/ML Readiness

Education

Bachelor’s degree in CS/IS/Engineering/Finance

Tools

Azure DevOps

Jira

SQL Server

SSIS

SSAS

Power BI

Job description

Section 1: Position Summary

The Director, Data Solutions leads the data foundation for Ascensus Data & Analytics: the architecture, engineering, integration and quality work that enterprise reporting, client-experience analytics and predictive modeling all depend on. The role is accountable for connecting disparate source systems into a connected intelligence layer, so insight can be drawn across the enterprise rather than one system at a time, and so analytics teams build on trusted data instead of assembling it themselves.

The Director reports to the Senior Vice President, Data & Analytics and leads a team spanning data engineering, data product management and business analysis. The role works closely with Technology on data priorities, pipeline delivery and platform standards, starting with the Retirement business line, and with analytics leaders and business stakeholders on sequencing what gets built first.

Section 2: Job Functions, Essential Duties and Responsibilities

• Data Architecture & Technical Direction: Define the target architecture for our shared data environment, including the layered (medallion) structure, semantic models and reporting layers, working with Technology and enterprise architecture. Set standards for pipeline design, source control, testing, deployment and documentation, and hold the team to them.

• Connected Data Sets & Integration: Build the integrated data sets that join sources across the enterprise. Near-term priorities include client interactions, book of business, customer experience and behavioral trends, and product and partner definitions with a reliable source of truth. Deliver the shared client-interaction source covering digital, workflows, interactions and sentiment, and the advisor data pipeline connecting core platform data with CRM sales activity.

• Process Efficiency & Automation: Automate the manual, repetitive reporting and data preparation work that consumes capacity today. Track cycle time and manual touchpoints on recurring deliverables and reduce both.

• Data Products & Roadmap: Work with the data product owner to maintain a prioritized data roadmap tied to analytics and business priorities. Build data sets once, document them, assign owners, and make them reusable across teams instead of rebuilt per project.

• Data Quality & Governance: Establish a single source of truth for enterprise reporting and client insight. Maintain enterprise data definitions so key terms mean the same thing across lines of business. Run data quality audits, and identify gaps in foundational systems that block enterprise-wide reporting and cross-sell insight.

• AI & Advanced Analytics Enablement: Structure and govern data to support predictive and propensity modeling and emerging agentic workflows, working with data science and analytics early enough that the foundation is ready when the models are.

• Technology Partnership: Act as the main point of coordination with Technology on data priorities, platform upgrades and the ongoing support model. Represent Data & Analytics requirements in architectural review and influence decisions so solutions stay consistent and scalable across teams.

• Team Leadership & Development: Lead, coach and develop a team spanning data engineering, data product management and business analysis. Set clear expectations for engineering discipline and delivery, mentor through code and design review, and build the bench as the function grows.

• Stakeholder Communication: Explain technical constraints and trade-­offs in plain business terms to analytics leaders, business stakeholders and executives, and communicate status, dependencies and risk early.

General

• Proactively adopt, protect and boldly embrace emerging technologies (AI, automation, etc.), platforms, and evolving practices that accelerate transformational growth and advance Ascensus’ mission and client commitments.
• Responsible for protecting, securing, and proper handling of all confidential data held by Ascensus to ensure against unauthorized access, improper transmission, and/or unapproved disclosure of information that could result in harm to Ascensus or our clients.
• At Ascensus we are guided by our Core Values of People Matter, Quality First and Integrity Always. They inspire us every day to prioritize an environment of respect for those we serve and one another and should be visible in your actions on a day-to-day.

Supervision

• Direct reports span Data Engineer, Data Product Owner and Business Analyst roles. Team composition and size will flex with business priorities.

Section 3: Experience, Skills, Knowledge Requirements

• Leadership & Experience: 10+ years in financial services in data engineering, data management, business intelligence or systems analysis, including at least three years managing a data or reporting team.
• Process Ownership: Experience owning a recurring, deadline-driven data production cycle, such as regulatory reporting, capital planning or stress testing submissions, financial close or executive reporting, with responsibility for streamlining it over successive cycles.
• Technical Expertise: Strong SQL, 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. Working familiarity with the Microsoft data stack in use at Ascensus (SQL Server, SSIS, SSAS, Power BI semantic models) is strongly preferred.
• Systems Analysis & Solution Engineering: Experience designing, developing, testing and administering internal applications and reporting tools, including replacing vendor products where an internal solution serves the business better.
• Delivery Discipline: Experience with software development lifecycle methodologies (Agile Scrum, Kanban) using tools such as Azure DevOps or Jira for requirements and delivery management.
• AI/ML Readiness: Practical understanding of what predictive, propensity and generative AI use cases require of upstream data, including structure, granularity, freshness, lineage and governance.
• Business & Industry Acumen: Retirement, wealth management, asset management, banking or fintech experience preferred. Able to connect data investments to commercial outcomes and explain the value and trade‑offs of foundational work to non-technical leaders.
• Collaboration & Influence: Proven ability to build working relationships across Technology, Operations, Product and business teams, and to exercise good judgment independently.
• Education: Bachelor’s degree in Computer Science, Information Systems, Engineering, Finance or a related field required, or equivalent work experience. Advanced degree or relevant data platform certifications preferred.

For all virtual remote positions, in order to ensure associates can effectively perform their job duties with no distractions, we require an uninterrupted virtual workspace and there is also an expectation of family care being in place during business hours. Additionally, there is an internet work speed requirement of 25 Mbps or better for individual use. If more than one person is utilizing the same internet connection in the same household or building, then a stronger connection is required.If you are unsure of your internet speed, please check with your service provider. Note: For call center roles specifically, it is a requirement to either hardwire your equipment directly to the internet router or ensure your workstation is in close proximity to the router. Please ensure that you are able to meet these expectations before applying.

We are proud to be an Equal Opportunity Employer