Data Engineer, Specialist - DADES3001

The Vanguard Group•Charlotte, PA

7d•Hybrid

About The Position

Build Data Products That Matter Design and build scalable data platforms, pipelines, and services that power analytics, business decisions, machine learning, and AI-enabled solutions. Transform complex, high-volume, and messy datasets into trusted, well-governed data products that teams depend on every day. Create reusable frameworks, APIs, and self-service capabilities that improve data accessibility and developer productivity. Design data models and architectures optimized for analytics, reporting, AI, and advanced data workloads. Continuously improve platform performance, scalability, reliability, observability, and security. Engineer Smarter with AI Leverage AI-assisted development tools to accelerate coding, testing, debugging, documentation, and solution design while maintaining high quality standards. Apply AI to eliminate repetitive work, automate engineering tasks, and improve delivery efficiency. Experiment with emerging AI engineering capabilities and identify opportunities to improve team productivity and software quality. Use engineering judgment to validate AI-generated outputs and ensure solutions remain secure, maintainable, and production-ready. Help define and evolve best practices for AI-augmented software development across the organization. Solve Complex Problems Tackle challenging data integration, data quality, performance, and reliability problems across enterprise-scale systems. Investigate production issues, identify root causes, and implement long-term solutions rather than temporary fixes. Design robust monitoring, alerting, lineage, and observability capabilities that increase trust in data. Improve processes and systems so that teams can focus more time on innovation and less time on operational overhead. Collaborate and Lead Partner with analysts, data scientists, architects, software engineers, and business stakeholders to deliver impactful solutions. Translate business objectives into scalable technical architectures and implementation strategies. Lead initiatives from design through deployment and operational support. Mentor engineers, share knowledge, conduct thoughtful code reviews, and help raise the engineering bar across the team. Contribute to innovation initiatives, proof-of-concepts, hackathons, and emerging technology experiments.

Requirements

8+ years of experience in data engineering, software engineering, analytics engineering, or related technical disciplines.
Proven experience designing and implementing scalable data pipelines and cloud-based data platforms.
Strong expertise in SQL, data modeling, and relational and non-relational database technologies.
Strong programming skills in Python and at least one additional language such as Scala or Java.
Experience with modern cloud platforms such as AWS, Azure, or GCP.
Hands-on experience with technologies such as Spark, Databricks, Snowflake, Kafka, Hadoop, or similar modern data platforms.
Strong understanding of data quality, governance, security, observability, lineage, and operational excellence practices.
Experience using AI coding assistants and AI-enabled engineering tools.
Curiosity about emerging technologies and a willingness to experiment with new approaches.
The ability to combine AI-generated outputs with sound engineering judgment.
A mindset focused on continuous learning, innovation, and continuous improvement.
Experience leading technical initiatives and influencing engineering decisions.
Strong communication and collaboration skills.
Ability to navigate ambiguity and transform ideas into practical solutions.
Passion for mentoring teammates and helping others grow.
A bias toward ownership, curiosity, and continuous improvement.

Nice To Haves

Machine learning, GenAI, agentic AI, or advanced analytics platforms.
Building data products that support AI and machine learning workloads.
Platform engineering, Infrastructure as Code, DevOps, and CI/CD automation.
Using AI to significantly improve engineering productivity, quality, or delivery outcomes.
Cloud, Databricks, Snowflake, or related technical certifications.
Open-source contributions, technical blogging, public speaking, or engineering community leadership.

Responsibilities

Design and build scalable data platforms, pipelines, and services.
Transform complex datasets into trusted data products.
Create reusable frameworks, APIs, and self-service capabilities.
Design data models and architectures optimized for analytics, reporting, AI, and advanced data workloads.
Continuously improve platform performance, scalability, reliability, observability, and security.
Leverage AI-assisted development tools to accelerate coding, testing, debugging, documentation, and solution design.
Apply AI to eliminate repetitive work and automate engineering tasks.
Experiment with emerging AI engineering capabilities.
Tackle challenging data integration, data quality, performance, and reliability problems.
Investigate production issues, identify root causes, and implement long-term solutions.
Design robust monitoring, alerting, lineage, and observability capabilities.
Improve processes and systems to increase innovation and reduce operational overhead.
Partner with analysts, data scientists, architects, software engineers, and business stakeholders.
Translate business objectives into scalable technical architectures and implementation strategies.
Lead initiatives from design through deployment and operational support.
Mentor engineers, share knowledge, and conduct code reviews.
Contribute to innovation initiatives, proof-of-concepts, hackathons, and emerging technology experiments.