Protective Life · Los Angeles
Software EngineeringData ScienceRemoteFull Time
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.
Individual contributor role with enterprise‑level impact; leads through influence, not through direct reporting relationships.
Serves as the recognized design authority for assigned data domains and initiatives, with the standing to approve, challenge, or redirect solution designs.
Operates with a high degree of autonomy on ambiguous, cross‑domain problems where requirements, ownership, and precedent are not yet established.
Sets direction that is adopted by multiple delivery teams and persists well beyond the life of any single project.
Escalates and resolves architectural disagreements across teams, bringing stakeholders to a defensible, documented decision.
Trusted to represent the data organization in enterprise forums, executive conversations, and vendor or partner discussions.
Technical Leadership and Direction
Set and communicate the technical direction for data solutions across assigned domains, establishing the patterns, standards, and reference designs that other teams build against.
Serve as design authority for significant data initiatives—reviewing, approving, and where necessary challenging designs proposed by delivery teams.
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.
Make and defend architectural decisions under uncertainty, documenting rationale, trade‑offs, and the conditions under which a decision should be revisited.
Anticipate downstream technical and organizational consequences of design choices, and proactively raise risks before they become delivery problems.
Represent data architecture positions credibly to senior technology and business leadership.
Data and System Solution Design
Design and implement end‑to‑end data solutions, including data warehouses, data lakes, and data pipelines, to support analytics, reporting, and AI initiatives.
Define logical and physical data models that balance business usability with technical efficiency.
Select and apply appropriate technologies (cloud platforms, database engines, orchestration tools) based on use case, scale, and cost considerations.
Business and Technical Translation
Partner with business stakeholders and leaders to understand objectives and translate them into clear technical requirements for data, reporting, and advanced analytics.
Frame technical options as business decisions—articulating cost, risk, time‑to‑value, and long‑term implications so leaders can choose with confidence.
Influence business stakeholders toward sustainable solutions, including the ability to constructively push back on requests that create long‑term cost, risk, or complexity.
Ensure data solutions support accuracy, accessibility, governance, and regulatory compliance.
Act as a design authority during delivery, ensuring solutions meet business intent while remaining technically sound.
Data Integration and Enablement
Design integration patterns to move and share data across systems using APIs, services, and pipelines.
Enable downstream consumption by analytics, BI, and AI platforms through well‑structured, well‑documented data products.
Reduce point‑to‑point integrations by promoting reusable, scalable data patterns and driving their adoption across teams.
Strategic Data Planning
Shape and influence multi‑year data roadmaps that prioritize high‑value use cases while avoiding data redundancy and unmanaged “data swamps.”
Partner with platform, governance, and enterprise architecture teams to align solution designs with enterprise standards and capabilities—and to evolve those standards where they no longer serve the business.
Identify opportunities to simplify, consolidate, or modernize existing data solutions, and build the business case needed to fund and sequence that work.
Advise leadership on build‑versus‑buy decisions, platform selection, and the architectural implications of strategic investments.
Security, Scalability, and Reliability
Design secure data environments that follow Protective’s security, privacy, and access‑control policies.
Ensure solutions scale to support increasing data volumes, complexity, and business demand.
Incorporate resiliency, monitoring, and operational considerations into solution designs.
Mentorship and Capability Building
Mentor data engineers and less experienced architects, raising the overall design and technical judgment of the data organization.
Lead design reviews and architecture working sessions that develop others while improving outcomes.
Create durable artifacts—reference architectures, design patterns, decision records, and documentation—that scale the architect’s judgment beyond their personal involvement.
Contribute to hiring, technical assessment, and the development of the broader data community of practice.
Technical Depth
Strong understanding of modern data architectures, including data lakes, data warehouses, and streaming or batch pipelines.
Proven ability to design solutions that integrate data across multiple systems and domains.
Ability to translate business problems into practical technical designs without over‑engineering.
Working knowledge of cloud‑native architectures, data security principles, and scalable design patterns.
Comfort operating in a delivery‑oriented environment alongside engineers, analysts, and product teams.
Leadership and Influence
Demonstrated ability to lead and drive outcomes without formal authority, earning credibility through expertise, consistency, and follow‑through.
Skilled at building consensus among stakeholders with different priorities, incentives, and technical perspectives—and at making a clear call when consensus is not achievable.
Able to communicate and defend technical positions effectively with audiences ranging from hands‑on engineers to senior business executives.
Comfortable operating amid ambiguity, competing demands, and incomplete information; brings structure and direction to unclear problem spaces.
Strong facilitation skills in design reviews, architecture forums, and cross‑functional working sessions.
Sound judgment about when to enforce a standard, when to allow a pragmatic exception, and when to escalate.
Genuine interest in developing others and strengthening the technical capability of the wider organization.
Required Qualifications
Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field, or equivalent experience.
Significant progressive experience in data engineering, data architecture, or solution architecture, including substantial time in a senior technical or architect‑level capacity.
Demonstrated experience serving as a technical leader or design authority on significant data initiatives spanning multiple teams or business domains.
Experience designing data solutions that support analytics, reporting, or AI/ML use cases.
Hands‑on experience with cloud data platforms and modern data technologies.
Proven track record of influencing technical direction and driving adoption of standards or patterns across teams the individual did not manage.
Experience partnering directly with business leadership to shape priorities, frame trade‑offs, and support investment decisions.
Strong communication skills with the ability to explain technical concepts to non‑technical stakeholders and to present recommendations to senior leadership.
Experience mentoring engineers or other architects.
Preferred Qualifications
Experience in financial services, insurance, or other regulated industries.
Familiarity with data governance, security, and compliance requirements, including experience partnering with governance and risk functions.
Experience supporting AI, machine learning, or advanced analytics solutions.
Background working in agile or product‑oriented delivery models.
Experience participating in or presenting to enterprise architecture review boards or similar governance forums.