# Data Engineer, Specialist at Vanguard

Vanguard is hiring a Data Engineer, Specialist in Hyderabad, India. Level rates it Works on AI ●●●○; you can [apply on Vanguard](https://jobsbylevel.com/go/028ba5a8-52bc-45d4-bbec-6075d1677829?utm_medium=ai-agent).

AI Level 3, AI centrality 60 out of 100. Hyderabad, India.

## Details

- Company: [Vanguard](https://jobsbylevel.com/companies/vanguard)
- AI level: AI Level 3 (score 60 out of 100)
- Location: Hyderabad, India
- Posted: October 26, 2026
- Apply: https://jobsbylevel.com/go/028ba5a8-52bc-45d4-bbec-6075d1677829?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=data-engineer-specialist-at-vanguard-ba3438

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## Description

About Vanguard Founded in 1975, Vanguard is one of the world's leading investment management companies. The firm offers investments, advice, and retirement services to tens of millions of individual investors around the globe—directly, through workplace plans, and through financial intermediaries. Vanguard India Vanguard’s office in India is a significant milestone in our global expansion. We are committed to establishing an enduring technology center in Hyderabad, Telangana and are excited to be adding talent who will focus on Artificial Intelligence (AI), mobile, and cloud-based technologies that drive our business outcomes and deliver a world-class experience for our clients. Role Summary The Data Engineer, Senior Specialist is responsible for designing, developing and optimizing data pipelines and architectures to support scalable and efficient data processing. This role involves working with complex datasets, integrating multiple data sources and ensuring high data quality and reliability. The role requires a strong product mindset to understand business needs, translate them into sustainable technical solutions, and take end-to-end ownership of delivery, quality, reliability, and continuous improvement. The individual will collaborate with business and technology stakeholders to deliver secure, high-quality, and future-ready data solutions while contributing to modern AI-assisted engineering practices. The role also carries technical ownership for business-critical data products and platforms, including production reliability, incident management, release support, operational improvements, and sustainable application-support practices. Responsibilites Develop and maintain efficient ETL (Extract, Transform, Load) pipelines that ingest, process and transform large datasets from diverse sources. Build and manage data storage solutions using relational and non-relational databases, ensuring high availability, performance and scalability. Work with APIs, third-party data sources and internal databases to create unified datasets that provide actionable insights for business stakeholders. Partner with business intelligence analysts, data scientists and software engineers to design and implement data-driven solutions that meet business requirements. Design optimized data schemas and structures that support analytical workloads and improve query performance. Leverage orchestration tools like Apache Airflow or Prefect to automate ETL workflows and improve operational efficiency. Provide technical guidance, review code and promote best practices within the data engineering team. Lead and mentor Data and AI Engineers, providing technical direction, coaching, and engineering guidance. Lead solution architecture and technical design, ensuring alignment with business objectives, product priorities, and engineering standards. Design and optimize scalable data pipelines, cloud data platforms, data models, and storage solutions supporting analytical and operational workloads. Architect reliable batch and real-time data processing solutions, incorporating data quality, governance, security, performance, and scalability. Partner with Product Owners, business stakeholders, Architects, Analysts, and Engineering teams to translate requirements into sustainable data solutions. Take end-to-end technical ownership across design, development, deployment, production reliability, and continuous improvement. Evaluate technical options and make informed architecture decisions considering performance, scalability, security, cost, reliability, and maintainability. Establish engineering and operational practices covering CI/CD, automation, testing, observability, monitoring, release readiness, and production support. Lead complex production incident resolution, root-cause analysis, preventive actions, and reliability improvements, while guiding engineers in troubleshooting and reducing operational risk. Drive engineering excellence through

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