DAE Manager

This is a leadership position, critical to Stori's business objectives as a FinTech company offering regulated financial products. The DAE Manager owns the definition and integrity of the financial and customer-engagement metrics the business runs on, and leads the analytics and data engineering team that produces them. The role combines three things: authority over metric definitions across business functions, ownership of the transformation pipelines and monitoring layer that serve them, and leadership of a small, high-leverage team that increasingly builds and supervises AI agents rather than doing every task by hand. The role holder exercises the highest level of authority over the structure, quality, security and accessibility of analytical data. Core responsibilities include managing and overseeing data operations, safeguarding sensitive company and customer information, and delivering high-impact reporting for executive decision-making. Required

• Financial industry experience. 3+ years working with financial products and their data (5+ years preferred) — credit, lending, deposits, payments or equivalent. Familiarity with the metrics and regulatory context of a regulated lender or Sofipo.
• People management track record. 2+ years managing people, with a demonstrated record of recruiting, developing and retaining junior analysts and growing them into independent contributors.
• Complex data pipeline development. 3+ years building and maintaining production transformation pipelines using dbt or an equivalent transformation framework, including modular model design, testing, incremental logic and dependency management at scale.
• Data quality monitoring. Proven ability to design automated data quality checks — freshness, completeness, referential integrity and business-rule validation — with alerting and documented remediation ownership.
• Technical depth. Expert command of SQL and Git, plus a core programming language (Python preferred). Hands-on experience with a professional-grade visualization tool such as Streamlit, QuickSight, Tableau, or Looker.
• Problem solving and critical thinking. Creative, resourceful and proactive in finding non-obvious solutions to complex problems; able to challenge assumptions and stress-test conclusions rather than accepting them at face value.
• Prioritization across stakeholders. Rigorous prioritization across multiple concurrent projects and competing stakeholders, with the judgment to say no, sequence work transparently and deliver on commitments in a fast-moving environment.
• Communication. Strong communication and presentation skills, sufficient to work credibly with cross-functional teams and senior management, and to translate business requirements into detailed data designs and technical specifications.

Preferred

• AI skills and agents. Experience building AI skills or agents — packaging domain knowledge and workflows into reusable, reliable automation. This is the strongest differentiator for this role.
• Data platform breadth. Working knowledge of data lineage, data quality assurance and data discovery tooling; experience with orchestration (Airflow / MWAA) and cloud data warehousing (Redshift or equivalent).

AI Skills

This role is expected to lead the team's transition toward AI-assisted and agentic data work. The following capabilities are evaluated directly:

• Agentic pipeline development. Designing AI agents that author, refactor and maintain transformation models and pipelines, including how such agents are scoped, given repository and schema context, constrained, and validated before their output is merged.
• Agentic data quality and monitoring. Using AI to automate validation and auditing of data quality at scale — detecting anomalies, drift and inconsistencies across large volumes of data — and to triage and explain failures rather than only flag them.
• Agent-built dashboards and reporting. Applying LLMs to generate and maintain business monitoring dashboards and recurring executive reporting from structured data, including SQL and code generation, with review gates that keep generated output trustworthy.
• Reusable skills and knowledge capture. Codifying team and business-domain knowledge — metric definitions, table semantics, analytical playbooks — into reusable AI skills so that institutional knowledge scales beyond individual analysts.
• Judgment on AI limits. Clear-eyed assessment of where agentic automation is and is not appropriate, particularly where regulated data, PII or executive-facing numbers are involved.

Information Access

The DAE Manager will hold unrestricted, highest-confidentiality access to the following databases and information sets, which is fundamental to the strategic control and management of the Sofipo:

• Complete transactional databases for financial products (loans, accounts, credit cards, balances, movements).
• Personally identifiable information (PII) and sensitive customer data, including demographic information, credit history and financial behavior patterns.
• Credit risk models, customer scoring and Customer Lifetime Value (CLV).
• Key performance indicator (KPI) results and internal financial metrics critical to executive decision-making.
• Marketing and performance campaign strategies and results at a granular level.

Given this level of access, the role carries a correspondingly high standard of confidentiality, data protection and regulatory compliance. • Metric definition and governance. Define, document and maintain the company's financial and customer-engagement metrics, and drive alignment on those definitions across Product, Finance, Risk, Marketing and Operations. Act as the arbiter when teams disagree on how a number is calculated, and ensure a single source of truth is upheld in the warehouse.

• Team leadership. Build and manage a small, highly efficient Analytics Engineer team supporting multiple business functions. Recruit, onboard and develop junior and mid-level analysts and analytics engineers; set standards for code review, documentation and delivery; and allocate capacity across competing business demands.
• AI agents for data pipelines and monitoring. Design and deploy AI agents that build and maintain transformation pipelines, automate data quality validation, and generate and maintain business monitoring dashboards. Define where agentic automation is appropriate, what human review gates apply, and how agent-produced work is tested before it reaches stakeholders.
• Data quality and reliability. Own the reliability of the analytical layer: design automated data quality checks covering freshness, completeness, referential integrity and business-rule validation, with alerting, documented remediation ownership and clear escalation paths when checks fail.
• Executive reporting. Produce and present dashboards, reports and analyses to senior management and key stakeholders, translating complex data into clear, actionable recommendations for executive decision-making.
• Data and AI roadmap. Create and maintain the data and AI roadmap that supports continuous business growth and product improvement, sequencing investment across pipelines, metric coverage, monitoring and agentic automation, and aligning it with company objectives.
• Data assurance and confidentiality. Guarantee the integrity, accuracy and confidentiality of data across all analytics activities, and uphold the highest standards of data stewardship in line with Sofipo regulatory obligations.