divh2Infinitive Architect/h2pInfinitive is a data AI consultancy that enables global brands to deliver results through insights, innovation, and efficiency. We possess deep industry and technology expertise to drive and sustain adoption of new capabilities. We match our people and personalities to our clients culture while bringing the right mix of talent and skills to enable high return on investment./ppInfinitive has been named Best Small Firms to Work For by Consulting Magazine eight times, and has also been named a Washington Post Top Workplace, Washington Business Journal Best Places to Work, and Virginia Business Best Places to Work./ph3Role Overview/h3pThis architect will define and shape a unified platform service that enables scalable, governed, and cost-efficient data access across the bank. The ideal candidate will influence enterprise design standards and technical adoption by making Databricks Unity Catalog the effortless, observable, and default foundation for data integration, governance, and analytics across all business domains./ph3Key Responsibilities/h3ulliDefine and champion the end-to-end architecture for the banks Databricks-based data platform, ensuring scalability, security, cost efficiency, and ease of adoption./liliDesign a self-service platform layer that leverages Databricks Unity Catalog to deliver seamless data discovery, access, and observability across all environments./liliEstablish architectural patterns and reference implementations that encourage enterprise-wide reuse and standardization./li/ulh3Unity Catalog Strategy Enablement/h3ulliLead the design and implementation of Databricks Unity Catalog as the central governance planedefining catalog hierarchies, fine-grained access controls, and cross-environment lineage./liliEvaluate and implement metadata, RBAC/ABAC, and data masking capabilities to meet regulatory and compliance requirements (e.g., GLBA, GDPR, HIPAA)./liliDefine the template architecture that allows Unity Catalog to operate as a scalable and cost-effective shared service across lines of business./li/ulh3Scalability, Cost, and Observability/h3ulliEngineer platform capabilities that provide deep visibility into compute, storage, and catalog operations through integrated observability, monitoring, and FinOps practices./liliDevelop resource optimization strategies to balance performance and cost while maintaining compliance and SLAs./liliEstablish metrics, dashboards, and alerts to ensure the platform scales predictably under enterprise workloads./li/ulh3API and Integration Design/h3ulliArchitect streamlined RESTful/GraphQL APIs for secure, governed data access and metadata integration./liliEnsure interoperability with enterprise systems, APIs, and external data consumers using modern, consistent, and documented integration patterns./li/ulh3Data Modeling Pipeline Strategy/h3ulliGuide teams in building Lakehouse-aligned data models that maximize reuse and governance./liliOversee design of ETL/ELT architectures (Spark, PySpark, SQL) that integrate seamlessly with Unity Catalog for lineage and access tracking./li/ulh3Collaboration Influence/h3ulliPartner with engineering, data science, and risk teams to align platform design with business outcomes and regulatory expectations./liliInfluence architecture steering committees and platform engineering groups to adopt the Databricks foundation as a managed, enterprise-wide service./liliPromote a culture of easy adoption through clear design patterns, documentation, and working sessions./li/ulh3Technical Leadership Mentorship/h3ulliMentor engineers and architects on Databricks, Unity Catalog, and best practices for cost, scale, and observability./liliContribute to internal architecture communities and upskill teams across multiple domains./li/ulh3Required Skills Qualifications/h3ulliEducation: Bachelors or Masters degree in Computer Science, Engineering, or related field./liliExperience: 8+ years in data architecture or platform engineering, including experience designing enterprise-scale, distributed data environments./liliDatabricks Expertise: Deep hands-on knowledge of Databricks, Delta Lake, Apache Spark, and Lakehouse principles./liliUnity Catalog Mastery: Demonstrated success architecting and operationalizing Databricks Unity Catalog for enterprise governance, metadata management, and access control./liliProgramming Data: Advanced proficiency in Python (PySpark) and SQL; experience with cloud data platforms (AWS, Azure, or GCP)./liliAPI Engineering: Strong background in API architecture (REST, GraphQL, OpenAPI) and applying best-in-class security and observability./liliGovernance Knowledge: Expert-level understanding of data governance frameworks, data quality management, and regulatory compliance./liliSoft Skills: Outstanding communication and influence skills, with ability to advocate for design principles across executive, technical, and risk audiences./li/ulh3Preferred Qualifications/h3ulliExperience deploying Databricks and cloud infrastructure using Terraform or IaC frameworks./liliFamiliarity with MLflow and model governance integration./liliRelevant certifications (Databricks Certified Data Engineer, AWS/Azure/GCP Architect)./liliExperience with real-time data streaming technologies (Kafka, Structured Streaming)./li/ul/div