# Manager, Data Engineering at Vanguard

Vanguard is hiring a Manager, Data Engineering in Hyderabad, India. Level rates it Little AI ●○○○; you can [apply on Vanguard](https://jobsbylevel.com/go/eb9336c0-ed50-4283-a259-26d308bbcfdf?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 23, 2026
- Apply: https://jobsbylevel.com/go/eb9336c0-ed50-4283-a259-26d308bbcfdf?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=manager-data-engineering-at-vanguard-f4c43e

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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 Manager, Data Engineering is responsible for leading a team of data engineers to design, develop, and optimize scalable data pipelines and infrastructure. This role focuses on enabling high-performance data workflows, ensuring seamless data integration and driving automation to enhance data accessibility. Responsibilites Hire, mentor and manage a team of data engineers, fostering a culture of innovation, collaboration and continuous learning in data engineering best practices. Design, develop and maintain scalable ETL (Extract, Transform, Load) pipelines to efficiently process and integrate structured and unstructured data from multiple sources. Build and maintain scalable, fault-tolerant data architectures, ensuring efficient data storage, retrieval and processing across cloud and on-premises environments. Oversee the design, implementation and performance tuning of relational and NoSQL databases, ensuring data consistency, security and accessibility. Automate data ingestion, transformation and validation processes to enhance data pipeline efficiency and reduce manual intervention. Develop cloud-based data solutions using platforms like AWS, Azure, or Google Cloud, leveraging technologies such as Spark, Hadoop and Kafka for large-scale data processing. Work closely with data scientists, analysts and business intelligence teams to understand data requirements and develop scalable solutions for analytics and reporting. Proactively monitor data pipelines and systems for performance issues, troubleshoot failures and implement preventive measures for system reliability. Partner with business and IT leaders to align data engineering initiatives with company goals, ensuring seamless integration of data-driven solutions across departments. Lead, coach, and develop high-performing data and technology teams, including hiring, performance management, workforce planning, and talent development. Drive delivery of strategic initiatives across data engineering, data warehousing, data lakes / lakehouses, data integration, data governance, data quality, analytics, and business intelligence. Partner with business, product, architecture, and engineering stakeholders to translate priorities into product roadmaps, delivery plans, and measurable outcomes. Lead product-oriented teams with end-to-end accountability for the development, delivery, maintenance, production support, reliability, and continuous improvement of data products. Drive Agile practices, including intake management, backlog prioritization, estimation, sprint and release planning, and delivery governance. Manage capacity planning, resource forecasting, delivery commitments, risks, issues, and cross-team dependencies to enable predictable execution. Leverage AI-enabled tools and capabilities to improve personal and team productivity, automate and streamline day-to-day workflows, and accelerate responsible AI adoption across the team by identifying practical use cases, encouraging experimentation, and embedding effective AI practices into how the team works. Ensure data solutions are scalable, secure, reliable, reusable, and compliant with applicable technology, risk, privacy, and information security standards. Promote effective data governance, data quality, metadata management, lineage,

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