Data Solutions Architect
The Data Solutions Architect is a senior individual contributor who serves as a bridge between business strategy and technical execution, designing end‑to‑end data solutions that enable analytics, reporting, and AI use cases across the enterprise. This role combines deep data architecture expertise with solution design and technical leadership responsibilities—creating practical, scalable, and secure data solutions that translate business needs into actionable technical roadmaps.
This position is embedded closer to delivery teams, working hands‑on with business partners, engineers, and product teams to ensure data solutions are usable, compliant, and aligned to Protective’s priorities.
While this role has no direct reports, it carries significant leadership accountability. The Data Solutions Architect is expected to set technical direction, drive alignment across independent teams, and influence outcomes through expertise, credibility, and relationships rather than positional authority. Decisions made in this role shape multi‑year data investments, establish patterns adopted by multiple engineering teams, and directly affect Protective’s ability to deliver trusted data and AI capabilities at scale.
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Individual contributor role with enterprise‑level impact; leads through influence, not through direct reporting relationships.
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Serves as the recognized design authority for assigned data domains and initiatives, with the standing to approve, challenge, or redirect solution designs.
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Operates with a high degree of autonomy on ambiguous, cross‑domain problems where requirements, ownership, and precedent are not yet established.
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Sets direction that is adopted by multiple delivery teams and persists well beyond the life of any single project.
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Escalates and resolves architectural disagreements across teams, bringing stakeholders to a defensible, documented decision.
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Trusted to represent the data organization in enterprise forums, executive conversations, and vendor or partner discussions.
Technical Leadership and Direction
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Set and communicate the technical direction for data solutions across assigned domains, establishing the patterns, standards, and reference designs that other teams build against.
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Serve as design authority for significant data initiatives—reviewing, approving, and where necessary challenging designs proposed by delivery teams.
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Drive alignment across teams with competing priorities, differing technical opinions, and separate reporting lines, building consensus where possible and making clear recommendations where consensus is not reachable.
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Make and defend architectural decisions under uncertainty, documenting rationale, trade‑offs, and the conditions under which a decision should be revisited.
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Anticipate downstream technical and organizational consequences of design choices, and proactively raise risks before they become delivery problems.
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Represent data architecture positions credibly to senior technology and business leadership.
Data and System Solution Design
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Design and implement end‑to‑end data solutions, including data warehouses, data lakes, and data pipelines, to support analytics, reporting, and AI initiatives.
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Define logical and physical data models that balance business usability with technical efficiency.
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Select and apply appropriate technologies (cloud platforms, database engines, orchestration tools) based on use case, scale, and cost considerations.
Business and Technical Translation
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Partner with business stakeholders and leaders to understand objectives and translate them into clear technical requirements for data, reporting, and advanced analytics.
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Frame technical options as business decisions—articulating cost, risk, time‑to‑value, and long‑term implications so leaders can choose with confidence.
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Influence business stakeholders toward sustainable solutions, including the ability to constructively push back on requests that create long‑term cost, risk, or complexity.
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Ensure data solutions support accuracy, accessibility, governance, and regulatory compliance.
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Act as a design authority during delivery, ensuring solutions meet business intent while remaining technically sound.
Data Integration and Enablement
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Design integration patterns to move and share data across systems using APIs, services, and pipelines.
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Enable downstream consumption by analytics, BI, and AI platforms through well‑structured, well‑documented data products.
