# Data Engineer, Specialist at Vanguard

Vanguard is hiring a Data Engineer, Specialist in Hyderabad, India. Level rates it Little AI ●○○○; you can [apply on Vanguard](https://jobsbylevel.com/go/9559a189-f111-497f-b089-30fb57a3dd88?utm_medium=ai-agent).

AI Level 1, AI centrality 10 out of 100. Hyderabad, India.

## Details

- Company: [Vanguard](https://jobsbylevel.com/companies/vanguard)
- AI level: AI Level 1 (score 10 out of 100)
- Location: Hyderabad, India
- Posted: October 6, 2026
- Apply: https://jobsbylevel.com/go/9559a189-f111-497f-b089-30fb57a3dd88?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=data-engineer-specialist-at-vanguard-fc6f6f

## Prepare for this job

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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 We are seeking a Data Engineer to design, build and optimise scalable data solutions that power analytics, business intelligence and data-driven decision making across the enterprise. This role will partner closely with data analysts, data scientists, software engineers and business stakeholders to deliver reliable, high-quality data products and modern data platforms. The ideal candidate combines strong technical expertise in data engineering with the ability to translate business needs into scalable, efficient and maintainable solutions. Responsibilities Design, develop and maintain scalable ETL/ELT pipelines for processing large and complex datasets. Build, optimize and support data architectures, data models and storage solutions that enable efficient analytics and reporting. Integrate data from internal systems, APIs and third-party platforms to create trusted and actionable datasets. Develop and maintain solutions for both batch and real-time data processing. Ensure data quality, consistency, reliability and availability across enterprise data platforms. Collaborate with business stakeholders and technical teams to understand requirements and deliver data-driven solutions. Translate business specifications into technical designs, code and scalable data solutions. Leverage orchestration and workflow tools such as Apache Airflow or Prefect to automate and manage data pipelines. Perform data validation, testing and troubleshooting to ensure accuracy and integrity of data assets. Lead all phases of solution development, from design through implementation and production support. Conduct code reviews and promote engineering best practices across the team. Mentor and develop junior data engineers while contributing to technical standards and continuous improvement initiatives. Support platform upgrades, software releases and production deployments. 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 architecture/design/code reviews, reusable patterns,

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