Data Engineer

VANGUARD CHARITABLE ENDOWMENT PROGRAM•Malvern, PA

28d•Hybrid

About The Position

We are seeking a forward-thinking Data Engineer to help architect and build the foundation of Vanguard Charitable’ s next-generation business intelligence platform in AWS. This role is pivotal in shaping our modern data stack—designing scalable pipelines, implementing a data catalog, and building bronze, silver, and gold data layers to support analytics, reporting, and data science. You’ll work closely with a Senior Data Architect and our Data Science team and play a key role in building out our cloud-native Data Lakehouse architecture. This is a high-impact opportunity to influence the future of charitable giving through technology.

Requirements

Bachelor’s or master’s degree in computer science, Data Engineering, Statistics, or a related field.
5+ years of experience in data engineering, with a strong focus on cloud-native architectures (AWS preferred).
Proven experience building and maintaining Data Lakehouse environments using modern data stack tools.
Proficiency in BASH, Python and SQL; experience with Snowflake and dbt is a plus.
Strong experience and understanding of data lakehouse file and table formats (parquest, iceberg)
strong automation skills for operations and deployment functions, experience with CI/CD platforms like Jenkins
Experience with EDA (event driven architecture) for use in pipelines (i.e. SQS, SNS, Event Bridge)
understanding of DAG (Directed Acyclic Graph) for ETL workflow orchestrations
Familiarity with business data catalog platforms (e.g., Atlan, OvalEdge, Data.World)
Familiarity with technical data catalog with Iceberg enablement (AWS Glue, Snowflake, Polaris, etc)
Hands-on experience with Tableau, including supporting data scientists and analysts in building performant dashboards and visualizations.
Strong understanding of data governance, metadata management, and data quality frameworks.

Nice To Haves

Familiarity with donor-advised funds (DAFs), complex asset management, and charitable giving strategies is a plus.

Responsibilities

Design and build robust, scalable batch and streaming ETL/ELT pipelines in AWS using tools like DMS, AppFlow,[Di1] Glue, Lambda, Step Functions
Lead the development of Data Lakehouse architecture, integrating structured and semi-structured data into a unified analytics platform.
Implement and manage a business data catalog[Di2] to support data discovery, governance, and lineage.
Develop and maintain medallion architecture (bronze, silver, gold data layers) to support analytics and machine learning.
Collaborate with the Senior Data Architect to align with enterprise architecture and data modeling standards.
Partner with the Data Science team to enable predictive analytics and advanced modeling.
Support the Data Science team’s use of Tableau, including data preparation, dashboard optimization, and integration with curated datasets.
Monitor and optimize data pipelines for performance, reliability, and cost-efficiency.
Document data workflows, dbt models, and transformation logic for transparency and maintainability.
Stay current with modern data stack trends and continuously improve our data platform.