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.