Vice President - Core Engineering (Data Architect)

Role Overview: As a Senior Data Engineer / Data Architect at Goldman Sachs, you will serve as a technical leader and subject matter expert, responsible for defining, designing, and overseeing the implementation of enterprise-level data strategies, architectures, and solutions. Your role will involve managing complex data environments, aligning data initiatives with business objectives, and leading cross-functional teams to foster a data-driven culture. You will ensure data integrity, accessibility, security, and compliance across the organization's data assets. Key Responsibilities: - Lead the development and execution of the organization's overarching data strategy and architectural roadmap, including data governance frameworks, data modeling, data warehousing, data lakes, and real-time data platforms. - Architect and design highly scalable, robust, and fault-tolerant data pipelines and platforms for large-scale data ingestion, processing, storage, and consumption. - Define and implement comprehensive data governance policies, standards, and best practices to ensure data quality, consistency, security, privacy, and regulatory compliance. - Lead the design and maintenance of conceptual, logical, and physical data models for various data stores, optimizing for performance, scalability, and flexibility. - Continuously evaluate emerging data technologies, tools, and industry trends to recommend and lead their adoption, driving innovation and efficiency within the data landscape. - Proactively identify and resolve complex data-related performance bottlenecks, ensuring optimal performance and scalability of data platforms and solutions. - Provide expert technical guidance, mentorship, and leadership to data engineering and analytics teams, fostering best practices in data architecture and operational excellence. - Collaborate extensively with business stakeholders, product teams, software engineers, and IT operations to translate complex business requirements into effective data solutions. - Oversee the resolution of critical data-related incidents, perform root cause analysis, and implement preventative measures to ensure high availability and reliability of data systems. Qualifications: - 14+ years of progressive experience in Data Engineering, Data Architecture, or related roles. - Bachelor's or Master's degree in Computer Science, Software Engineering, Information Technology, or a related quantitative field. - Expert-level proficiency with major cloud providers and their data services. - Deep expertise in big data technologies such as Apache Spark, Hadoop, Kafka, and distributed processing systems. - Proven experience with data warehousing and data lake/lakehouse architectures. - Strong programming skills in languages commonly used in data engineering. - Extensive experience in designing, building, and optimizing complex ETL/ELT pipelines and data integration processes. - Mastery of various data modeling techniques and tools. - Strong understanding of various database technologies and data platforms. - Familiarity with IaC tools for managing data infrastructure. - Understanding and experience with DevOps, DataOps, and MLOps principles and practices. - Exceptional communication, presentation, and interpersonal skills. Preferred Qualifications: - Experience in specific industry domains. - Certifications in relevant cloud data platforms. - Experience with data visualization tools. - Familiarity with master data management and data quality tools. - Understanding of machine learning workflows and MLOps. Note: The above details are based on the job description provided by Goldman Sachs.,