Vice President Core Engineering (Data Architect)
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Posted on:
30+ days ago
Vacancies:
1 Vacancy
Job Summary
Description
What We Do
At Goldman Sachs our Engineers dont just make things we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems architect low latency infrastructure solutions proactively guard against cyber threats and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses transform finance and explore a world of opportunity at the speed of markets.
Engineering which is comprised of our Technology Division and global strategists groups is at the critical center of our business and our dynamic environment requires innovative strategic thinking and immediate real solutions. Want to push the limit of digital possibilities Start here.
Who We Look For
Goldman Sachs Engineers are innovators and problem-solvers building solutions in risk management big data mobile and more. We look for creative collaborators who evolve adapt to change and thrive in a fast-paced global environment.
Job Summary:The Senior Data Engineer / Data Architect 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. This role demands extensive experience in managing complex data environments a strategic mindset to align data initiatives with business objectives and the ability to lead mentor and influence cross-functional teams to foster a data-driven culture. The incumbent will ensure data integrity accessibility security and compliance across the organizations data assets.
Key Responsibilities:
- Strategic Data Architecture & Roadmapping:Lead the development and execution of the organizations overarching data strategy and architectural roadmap including data governance frameworks data modeling data warehousing data lakes and real-time data platforms.
- Complex Data System Design & Implementation:Architect and design highly scalable robust and fault-tolerant data pipelines and platforms for large-scale data ingestion processing (batch and real-time) storage and consumption. This includes selecting appropriate technologies and patterns (e.g. data mesh data fabric zero-ETL).
- Data Governance & Quality Leadership:Define and implement comprehensive data governance policies standards and best practices to ensure data quality consistency security privacy and regulatory compliance (e.g. GDPR HIPAA). This includes designing and maintaining data catalogs and metadata management.
- Advanced Data Modeling & Database Design:Lead the design and maintenance of conceptual logical and physical data models for various data stores optimizing for performance scalability and flexibility.
- Technology Evaluation & Adoption:Continuously evaluate emerging data technologies tools and industry trends (e.g. open table formats AI-powered data tools) to recommend and lead their adoption driving innovation and efficiency within the data landscape.
- Performance Optimization & Scalability:Proactively identify and resolve complex data-related performance bottlenecks ensuring optimal performance and scalability of data platforms and solutions to support growing data volumes and analytical demands.
- Technical Leadership & Mentorship:Provide expert technical guidance mentorship and leadership to data engineering and analytics teams fostering best practices in data architecture development and operational excellence.
- Cross-Functional Collaboration & Stakeholder Management:Collaborate extensively with business stakeholders product teams software engineers and IT operations to translate complex business requirements into effective data solutions and ensure alignment with organizational goals.
- Incident Response & Troubleshooting: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:
- Experience:14 years of progressive experience in Data Engineering Data Architecture or related roles with a strong focus on designing and implementing large-scale enterprise-level data solutions.
- Education:Bachelors or Masters degree in Computer Science Software Engineering Information Technology or a related quantitative field.
- Cloud Platforms:Expert-level proficiency and extensive hands-on experience with major cloud providers (e.g. AWS GCP Azure) and their data services (e.g. AWS S3 Redshift Glue EMR; GCP BigQuery Dataflow Dataproc; Azure Data Lake Synapse Databricks).
- Big Data Technologies:Deep expertise in big data technologies such as Apache Spark Hadoop Kafka and distributed processing systems.
- Data Warehousing & Data Lakes:Proven experience with data warehousing (e.g. Snowflake Redshift BigQuery Synapse Analytics) and data lake/lakehouse architectures (e.g. Delta Lake Azure Data Lake Storage).
- Programming & Scripting:Strong programming skills in languages commonly used in data engineering (e.g. Python Scala Java SQL).
- ETL/ELT & Data Integration:Extensive experience in designing building and optimizing complex ETL/ELT pipelines and data integration processes.
- Data Modeling:Mastery of various data modeling techniques (e.g. dimensional modeling relational modeling) and tools.
- Databases:Strong understanding of various database technologies (SQL and NoSQL) and data platforms especially in high-performance high-availability contexts.
- Infrastructure as Code (IaC):Familiarity with IaC tools (e.g. Terraform CloudFormation Ansible) for managing data infrastructure.
- DevOps/DataOps/MLOps:Understanding and experience with DevOps DataOps and MLOps principles and practices for automated data and model deployment.
- Communication & Leadership:Exceptional communication presentation and interpersonal skills with a proven ability to influence stakeholders at all levels and lead technical teams.
Preferred Qualifications:
- Experience in specific industry domains (e.g. finance healthcare e-commerce).
- Certifications in relevant cloud data platforms (e.g. AWS Certified Data Analytics Google Cloud Professional Data Engineer Azure Data Engineer Associate).
- Experience with data visualization tools (e.g. Tableau Power BI).
- Familiarity with master data management (MDM) and data quality tools.
- Understanding of machine learning workflows and MLOps.
ABOUT GOLDMAN SACHS
At Goldman Sachs we commit our people capital and ideas to help our clients shareholders and the communities we serve to grow. Founded in 1869 we are a leading global investment banking securities and investment management firm. Headquartered in New York we maintain offices around the world.
We believe who you are makes you better at what you do. Were committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally from our training and development opportunities and firmwide networks to benefits wellness and personal finance offerings and mindfulness programs. Learn more about our culture benefits and people at Were committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: The Goldman Sachs Group Inc. 2023. All rights reserved.
Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race color religion sex national origin age veterans status disability or any other characteristic protected by applicable law.
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About Company
The Goldman Sachs Group, Inc. is a leading global investment banking, securities, and asset and wealth management firm that provides a wide range of financial services.
