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AVP Cloud Data Analytics ArchitectureGMAC Financial Services • United States
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AVP Cloud Data Analytics Architecture

GMAC Financial Services
  • US
    United States
  • US
    United States
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Über

AVP Cloud Data Analytics Architecture
Innovation isn't just a talking point at GM Financial, it's how we operate. From generative AI and cloud-native technologies to peer-led learning and hackathons, our tech teams are building real solutions that make a difference. We're committed to AI-powered transformation, using advanced machine learning and automation to help us reimagine customer interactions and modernize operations, positioning GM Financial as a leader in digital innovation within a dynamic industry. Join us and discover a workplace where your ideas matter, your development is prioritized, and you can truly make a global impact. This AVP Cloud Data Analytics Architecture will lead the cloud data architecture team and help scale the Data and Analytics organization for various finance groups. As an experienced cloud data architect, this role will work with the business to gather data and analytical requirements; design, develop and deploy Enterprise Cloud Data solutions; integrate data from disparate sources to load to the cloud, hybrid and multi-cloud; design, develop and monitor processes to transfer data between cloud systems and/or external vendors; collaborate with technical leaders to define api-first, web services, event based processes to maintain accuracy, lineage, metadata, integrity and efficiency of data across multiple levels of curation and consumption; provide data modeling standards, frameworks and templates to support the business; organize, catalog and define enterprise data to support AI, Machine Learning, Data Science and Reporting. The AVP will have hands-on experience in cloud data architecture to support Advanced Analytics in Azure, Databricks, APIs, microservices, and event driven architecture. The AVP will ensure the cloud, data, Machine Learning and AI platform is scalable and secure to meet future growth and requirements of business domains. This AVP will lead the strategy, development, and delivery of Finance data assets within the Finance Center of Excellence (COE), while managing a team of data engineers responsible for data engineering, analysis and reporting. The role is accountable for driving the data engineering roadmap, implementing process improvements, and delivering scalable SQL-based reporting and data solutions. Responsibilities
In this role, you will: Architect the data and analytics platform to support Company's vision, goals and strategies Develop cloud architecture solutions and design for data, machine learning, artificial intelligence and analytics using Azure, Informatica and Databricks Partner with the Finance business teams to design and implement governed, finance‑critical data products—ensuring accuracy, stewardship alignment, and seamless integration with enterprise data engineering standards Enable various Finance initiatives by engineering scalable, reliable datasets that support Securitization, FP&A reporting, Oracle Fusion–based workflows, Gen‑AI Finance Assistant use cases, and advanced analytics across Finance Translate broad strategies into specific data architecture plans, utilizing existing resources and information to achieve strategic objectives and improve business results Collaborate with Data Leadership to define cloud data architecture, business, Digital Transformation and Data & Analytics priorities and goals Collaborate with VP Cloud Data Analytics Architecture on department's performance to ensure accountability for achieving business results Architect the flow of data from transactional systems, data management and master data layer, to the cloud data and analytics platform and consuming applications Collaborate with various VP across finance groups to define and report the needs of product/architecture releases with respect to business objectives, security, data dependency, compliance, and timeliness of releases Architect and develop consistent metrics to measure data quality, security, utilization and consumption for management and audit Collaborate with business and technical teams to develop end-to-end Enterprise solutions for data, analytics, machine learning, artificial intelligence in the cloud Coach, mentor, and train data engineering team members to establish a consistent level of quality, accuracy, accountability and compliance with department standards Assist leadership in determining the annual business plan and setting the budgetary requirements for the department and manage each plan to ensure compliance and completion Champion an environment that promotes trust, continuous improvement, innovation, quality outcomes and self-development Develop relationships with key customer business and technical decision makers: drive long-term cloud data adoption within the company; enable cloud data advocacy Share insights and best practices, and connect with teams to remove key blockers What makes you a dream candidate? Advanced knowledge of cloud data architecture to support modeling, reporting, machine learning, artificial intelligence and analytics Advanced knowledge of cloud and data security methodologies, policies, standards and best practices Advanced knowledge of best practices in cloud data governance, architecture and tools for regulatory landscape for financial institutions Knowledge of GM Financial's core business functions, policies and procedures Advanced knowledge of cloud data architecture, data operations, data engineering, full-stack (dev ops, data dev ops, and dev sec ops) is required In-depth knowledge of cloud data security frameworks is required Wide-ranging understanding of general information technology standards and the Company's systems, such as Provenir, CPW, General Ledger, Oracle ERP, etc. Advanced knowledge of Azure Data Architecture - Azure Data factory, Azure Data Lake, Microsoft Synapse, Databricks and PySpark utilizing structured and unstructured data Knowledge of developing data engineering solutions in Python is required Knowledge of creating cloud MDM, CDC, Data Lineage, Metadata Management solutions is required Knowledge of utilizing SQL to transform, transport, copy and export data in the cloud is required Knowledge of developing and optimizing data pipelines from source to target systems is required Knowledge of transforming and curating multiple data types in Databricks is required Knowledge of event driven data architecture in the cloud is required Knowledge of utilizing APIs and web services in the cloud (integrate systems, platforms and data sources) is required Experience developing data solutions in the cloud for Marketing, Customer Experience, Data Science, Finance and Treasury is required Experience developing cloud data domains, such as customer360 Knowledge of industry-standard enterprise data management and integration technologies and methodologies, such as Informatica, is required Knowledge of Agile SAFe methodologies and the software development life cycle is required Advanced working knowledge of information systems and operations is required Experience working with transactional, temporal, time series, structured and unstructured data in the cloud is required Experience with large-scale enterprise-wide migration of workloads to the cloud required Effective written and verbal presentation skills with an ability to communicate complex technology, architecture, tools, processes and solutions with senior management Ability to interact collaboratively with internal customers and external vendors on highly complex enterprise cloud data and platform strategies Demonstrated ability to effectively lead, motivate, challenge, organize and supervise a team of data architects and full stack engineers Demonstrated quantitative skills and ability to apply complex cloud data architecture principles Demonstrated expertise in leading distributed teams of engineers and architects to align on key architectural and technical decisions and direction – and guiding those through successful execution Experience architecting large sophisticated transactional systems with high volume and high-performance requirements in the cloud – Azure, AWS, Google Cloud Proven cloud knowledge and deep understanding of Azure services – Azure Data Factory, Service Bus, ADLS2, Delta Lake, Cosmos DB, Synapse Ability to orchestrate, lead, and influence teams, ensuring successful implementation of cloud data projects Open-Source Tools in an Azure, AWS and/or Google Cloud is required Azure Data, Data Design and Curation required to support Advanced Analytics (Machine Learning, Risk, Artificial Intelligence) is required Traditional RDBMS (Oracle, Teradata, DB2) is required Qualifications
Experience: High School Diploma or equivalent required Bachelor's Degree in related field or equivalent work experience required Master's Degree in related field preferred 7-10 years of experience in building enterprise scale cloud data architecture and applications to support machine learning, artificial intelligence and analytics required 7-10 years of cloud application development solutions, PaaS, SaaS, IaaS, Serverless, Data Orchestration, API Management required 7-10 years of experience with scalable architectures using Azure App
  • United States

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