Vice President - Core Engineering (Data Architect)

Goldman Sachs; Bengaluru; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Goldman Sachs is looking for a Senior Data Engineer/Data Architect to lead the implementation of enterprise-level data strategies and oversee data integrity, accessibility, security, and compliance. You will need to bring 14+ years of relevant experience in data engineering, data architecture, and extensive knowledge of cloud platforms and big data technologies.

The position requires strong leadership skills and the ability to influence cross-functional teams to foster a data-driven culture. Join us to make a significant impact in designing robust data solutions.

Qualifications

• 14+ years of experience in Data Engineering, Data Architecture, or related roles with a focus on enterprise-level data solutions.
• Expert-level proficiency with major cloud providers and their data services.
• Deep expertise in big data technologies like Apache Spark, Hadoop, and Kafka.
• Proven experience with data warehousing and lake architectures.
• Strong programming skills in Python, Scala, Java, and SQL.

Responsibilities

• Lead the development of the organization’s data strategy and architectural roadmap.
• Architect and design scalable, robust data pipelines for large-scale processing.
• Define and implement data governance policies and best practices.
• Collaborate with stakeholders to translate business requirements into data solutions.
• Oversee the resolution of data-related incidents and ensure system reliability.

Skills

Data Engineering

Cloud Platforms (AWS, GCP, Azure)

Big Data Technologies (Apache Spark, Hadoop, Kafka)

Data Warehousing & Data Lakes

Programming (Python, Scala, Java, SQL)

ETL/ELT & Data Integration

Data Modeling

SQL and NoSQL Databases

Infrastructure as Code (IaC)

DevOps/DataOps/MLOps

Communication & Leadership

Education

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Technology

Job description

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 require 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. You will play a crucial role in ensuring 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 (batch and real‑time), 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 drive their adoption, fostering 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, development, and operational excellence.
• 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.
• 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.

Qualification Required

• 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: Bachelor’s or Master’s 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 (AWS, GCP, Azure) and their data services.
• 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 and data lake/lakehouse architectures.
• Programming & Scripting: Strong programming skills in languages commonly used in data engineering (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 and tools.
• Databases: Strong understanding of various database technologies (SQL and NoSQL) and data platforms.
• Infrastructure as Code (IaC): Familiarity with IaC tools for managing data infrastructure.
• DevOps/DataOps/MLOps: Understanding and experience with DevOps, DataOps, and MLOps principles and practices.
• Communication & Leadership: Exceptional communication, presentation, and interpersonal skills, with a proven ability to influence stakeholders at all levels and lead technical teams.