AVP of Data Architecture

Mariner Finance•Plano, TX

21h•Onsite

About The Position

The AVP of Data Architecture will drive the development, maintenance, and ongoing refinement of the company’s data architecture strategy, standards, principles, patterns, and roadmaps across enterprise data platforms, operational systems, integrations, analytics, reporting, and governance capabilities. This role involves partnering with technology, security, risk, compliance, analytics, and operations leaders to align data architecture recommendations with business objectives, transformation initiatives, regulatory expectations, enterprise technology standards, and long-term business needs. The position serves as a subject matter expert and trusted advisor for enterprise data architecture, providing guidance, evaluating data platform capabilities and emerging data technologies, and shaping secure, scalable, cost-effective, and sustainable data solutions. Leadership is exercised through influence, matrixed collaboration, and architecture governance to promote alignment, consistency, quality, and informed decision-making across various teams and stakeholders.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Management, Engineering, or related field; additional applicable experience may be substituted for a degree.
Minimum of ten (10) years of progressive experience in data architecture, data engineering, enterprise data platforms, business intelligence, analytics engineering, application architecture, or a related technology discipline.
Demonstrated experience working in a matrixed environment, leading complex cross-functional data architecture initiatives, influencing solution direction, and guiding architecture recommendations across multiple business and technology stakeholders.
Experience defining enterprise data architecture standards, data models, data flows, data integration patterns, and data platform strategies.
Strong experience designing conceptual, logical, and physical data models.
Experience with data warehouses, data lakes, cloud data platforms, operational data stores, reporting platforms, or analytics ecosystems.
Strong understanding of data governance, data quality, metadata management, data lineage, master data management, reference data, and data lifecycle management.
Experience designing and implementing integrations using APIs, ETL/ELT tools, middleware, event-driven architecture, streaming, replication, or comparable enterprise integration patterns.
Ability to translate business requirements into scalable, secure, and maintainable data architecture solutions.
Ability to develop strong relationships, influence outcomes, coach others, and partner with all levels across the organization.
Excellent interpersonal skills necessary to communicate professionally and effectively, verbally and in writing, with senior leadership, technology teams, business partners, vendors, and all levels of company staff.
Proven experience creating customer or business value through continuous improvement, developing strategies, leading complex projects, and delivering successful business outcomes.
Ability to articulate complex information in understandable terms to various audiences.
Comfortable presenting technical recommendations, performance data, risks, and solution options to leadership and across business functions.
Strong analytical thinking, problem-solving ability, and judgment, with the ability to evaluate tradeoffs across data architecture design, business value, technical complexity, cost, scalability, security, compliance, and operational risk.
Demonstrated ability to operate with significant autonomy, influence technical direction, exercise sound judgment, and provide expert guidance across teams.

Nice To Haves

Master’s degree in Computer Science, Information Systems, Data Management, Engineering, or related field.
Financial services experience, including consumer lending, loan origination, customer servicing, credit decisioning, regulatory reporting, or related data ecosystems.
Experience with cloud data platforms such as Snowflake, Databricks, Azure Synapse, AWS Redshift, Google BigQuery, or similar technologies.
Experience with data integration and data movement tools such as MuleSoft, Informatica, Boomi, Azure Data Factory, AWS Glue, Kafka, or similar technologies.
Experience supporting master data management, customer 360, enterprise reporting, regulatory reporting, data modernization, or data governance initiatives.
Experience with CRM, loan origination systems, customer servicing systems, data platforms, or third-party financial services providers.
Familiarity with modern architecture patterns, including API-first, event-driven architecture, microservices, cloud-native data platforms, and secure data exchange patterns.
Experience supporting architecture reviews, design governance, technical decision documentation, data quality remediation, and technical debt reduction.
Relevant certifications in data architecture, cloud architecture, data governance, analytics platforms, or enterprise architecture.

Responsibilities

Lead complex cross-functional data architecture initiatives by aligning stakeholders, guiding data architecture decision-making, resolving tradeoffs, and driving alignment across Technology, Data, Operations, Risk, Security, Compliance, Analytics, and business teams.
Partner with senior leaders to define short- and long-term enterprise data architecture plans that support current and future business needs, data platform modernization, data governance, reporting and analytics capabilities, regulatory expectations, and enterprise technology objectives.
Act as a trusted advisor to senior leadership on enterprise data architecture capabilities, data platform options, data integration approaches, source-of-truth decisions, implementation risks, data governance considerations, operational controls, and opportunities to improve business outcomes.
Develop and maintain enterprise data architecture standards, principles, patterns, roadmaps, and governance practices across operational systems, data platforms, analytics environments, reporting capabilities, and data integration layers.
Design conceptual, logical, and physical data models to support business operations, analytics, reporting, AI, regulatory needs, and enterprise technology initiatives.
Define and document data modeling standards, data integration patterns, data flow designs, metadata expectations, data lineage practices, source-of-truth guidance, and data quality principles.
Partner with business and technology stakeholders to align data architecture recommendations with business objectives, transformation initiatives, and regulatory expectations for current and future business needs.
Provide architecture guidance for data warehouses, data lakes, cloud data platforms, operational data stores, reporting platforms, analytics ecosystems, and related enterprise data capabilities.
Guide data flow, data integration, and data exchange patterns across enterprise platforms, including APIs, ETL/ELT, middleware, batch processing, event-driven architecture, real time streaming, replication, and third-party data exchanges.
Participate in architecture governance, review, and decision-making forums as the data architecture subject matter expert to promote scalable, secure, resilient, and consistent data solutions.
Evaluate current-state data architecture and identify opportunities for simplification, modernization, reuse, interoperability, performance improvement, and cost optimization within the enterprise data domain.
Evaluate and recommend data platforms, data tools, and data architecture patterns to support business needs, enterprise data strategy, and long-term sustainability.
Establish common data definitions, canonical data models, source-of-truth recommendations, and data ownership guidance across systems and business domains.
Partner with security, risk, compliance, legal, analytics, application, infrastructure, and integration teams to incorporate privacy, data protection, retention, audit, and regulatory requirements into data architecture recommendations.
Support data governance initiatives, including data classification, stewardship, data quality, metadata management, lineage, access, retention, and lifecycle management.
Deliver technical and non-technical architecture documentation for assigned initiatives, including data models, data flow diagrams, integration diagrams, lineage documentation, standards documentation, and architecture decision records.
Review project and solution designs to ensure alignment with enterprise data architecture standards, approved architecture, and long-term platform sustainability.
Identify data architecture risks, data quality issues, duplication, technical debt, and architectural improvement opportunities; recommend practical remediation plans in partnership with technology and business stakeholders.
Monitor, evaluate, and report on data architecture effectiveness, data quality metrics, data governance adoption, platform modernization progress, integration performance, business outcomes, and Key Performance Indicators.
Stay up to date on data architecture practices, emerging data technologies, industry standards, data governance expectations, privacy and security requirements, and changes in applicable regulations.
Promote technical growth and knowledge sharing among data engineers, architects, analysts, technical peers, and cross-functional partners by sharing expertise, providing guidance, and reinforcing consistent enterprise data architecture practices.
Communicate complex data architecture, data platform, integration, governance, and technology concepts in clear and understandable terms to technical audiences, business stakeholders, senior leadership, vendors, and governance partners.
Perform additional duties as assigned to support evolving business and technology needs.