Data Engineer

Vanguard•Malvern, PA

3d•Hybrid

About The Position

Vanguard is seeking an experienced Data Engineer to join its B2B AI and Data Engineering team. In this hands-on role, you will design, build, and operate scalable data pipelines and data products that support analytics, AI, and critical business functions. You will provide technical leadership, establish engineering standards, and partner with product, business, analytics, architecture, and security teams to deliver secure, reliable, and maintainable solutions. You will also contribute to modernization efforts by applying emerging AI and LLM capabilities where they create meaningful business or engineering value. The position involves developing cloud-native data solutions using AWS, creating analytics-ready models and curated data layers, and embedding data quality and governance practices into production solutions. You will support operational reliability through monitoring, incident response, root cause analysis, and continuous improvement. Vanguard describes its work model as hybrid for the majority of crew members, supporting flexibility alongside in-person learning, collaboration, and connection.

Requirements

At least 5 years of experience in data engineering, data analytics, programming, database administration, data management, or closely related technical work.
Hands-on experience developing production data solutions using Python and advanced SQL.
Experience with AWS data engineering, including AWS Glue and cloud-native or serverless data architectures.
Experience designing and supporting scalable data pipelines across ingestion, transformation, storage, and production operations.
Experience with data modeling, transformation patterns, and analytics-ready data structures.
Experience applying data quality and governance practices to production data solutions.
Experience developing data products, semantic layers, or curated consumption models for analytics, reporting, or AI use cases.
Experience implementing CI/CD and Infrastructure as Code using tools such as GitHub Actions, Terraform, or comparable technologies.
Working knowledge of Apache Airflow or AWS-managed Airflow workflows.
Undergraduate degree or an equivalent combination of training and experience.

Nice To Haves

Experience with additional AWS services such as Redshift Serverless, Lambda, S3, Step Functions, and SQS.
Experience with AI, agentic AI, Amazon Bedrock, knowledge graphs, Amazon Neptune, or related generative AI technologies and patterns.

Responsibilities

Design, build, and operate scalable data pipelines and data products across ingestion, transformation, storage, and production support.
Develop and optimize data engineering solutions using Python and advanced SQL, focusing on performance, cost efficiency, maintainability, and reliability.
Build cloud-native data solutions using AWS services and managed Airflow workflows.
Develop data models, transformations, semantic models, KPI frameworks, and curated data layers for trusted analytics, AI, and self-service consumption.
Implement and maintain CI/CD pipelines and Infrastructure as Code using GitHub Actions, Terraform, or comparable technologies.
Embed data quality and governance practices, including lineage, quality controls, access management, and auditability, into data products and engineering solutions.
Partner with product owners, business stakeholders, analytics teams, and architects to translate business requirements into scalable data products and actionable insights.
Mentor data engineers, lead design and code reviews, establish engineering best practices, and advance engineering quality across the team.
Participate in operational support, incident response, root cause analysis, monitoring, and continuous improvement to strengthen platform stability and reliability.
Apply automation, AI-assisted engineering, and emerging AI/LLM and agentic AI patterns where appropriate to improve development efficiency, standardization, and solution reliability.