Analytics Lead, Data Analytics & Modeling

The Vanguard Group•Charlotte, PA

16h•Hybrid

About The Position

This role involves hands-on technical leadership in designing, building, and maintaining scalable data models, datasets, and analytical solutions. The individual will write, review, and own production-quality SQL and Python code for data pipelines, transformations, and analytical workflows. They will serve as a technical lead on complex projects, defining approaches, driving design decisions, and ensuring high-quality execution. Collaboration with data engineering and platform teams is key to influencing data architecture, modeling standards, and tooling, ensuring analytics solutions are built on trusted, well-governed, and sustainable data foundations. The role also includes leading the delivery of high-impact dashboards, metrics, and analytical products to support decision-making, translating ambiguous business problems into structured analytical approaches, and applying advanced analytical techniques. Automation and standardization of reporting and analytics workflows are expected to improve scalability and reduce manual effort. Project leadership and mentorship of junior analysts are also core components, promoting best practices and acting as a bridge between business stakeholders and technical teams.

Requirements

4+ years of experience in data analytics, data engineering, or related technical roles with strong hands-on delivery.
Proven experience leading complex analytics or data initiatives as an individual contributor.
Strong experience designing scalable data models (e.g., dimensional, semantic, or analytical models).
Advanced proficiency in SQL and Python, with experience maintaining complex analytical codebases.
Experience partnering closely with data engineering teams on data pipelines, architecture, and data platforms.
Deep understanding of analytics and BI concepts, with experience using tools such as Power BI, Tableau, Looker, or similar.
Strong ability to translate ambiguous business problems into well-structured technical solutions.
Excellent communication skills, with the ability to clearly explain technical concepts to both technical and non-technical audiences.

Nice To Haves

Experience mentoring analysts in a technical lead or informal leadership capacity.
Familiarity with modern data platforms (e.g., cloud data warehouses, transformation frameworks, orchestration tools).
Experience operating in complex, enterprise or regulated environments.
Background in financial services, wealth management, or advisor-focused businesses.

Responsibilities

Design, build, and maintain scalable data models, datasets, and analytical solutions with a focus on correctness, performance, and reusability.
Write, review, and own production-quality SQL and Python code across data pipelines, transformations, and analytical workflows.
Serve as a technical lead on complex projects—defining approach, driving design decisions, and ensuring high-quality execution.
Partner closely with data engineering and platform teams to influence data architecture, modeling standards, and tooling.
Ensure analytics solutions are built on trusted, well-governed, and sustainable data foundations.
Lead the delivery of high-impact dashboards, metrics, and analytical products that support strategic and operational decision-making.
Translate ambiguous business problems into structured analytical approaches, data models, and technical solutions.
Apply advanced analytical techniques (e.g., statistical analysis, predictive modeling, machine learning where appropriate) to identify trends, risks, and opportunities.
Drive automation and standardization of reporting and analytics workflows to improve scalability and reduce manual effort.
Own outcomes for key deliverables, ensuring timelines, quality, and business impact are met.
Lead analytics workstreams across multiple concurrent initiatives, coordinating across stakeholders and ensuring alignment to business priorities.
Mentor and support junior analysts through code reviews, design discussions, and hands-on coaching.
Promote best practices in data modeling, coding standards, and analytical rigor across the team.
Act as a bridge between business stakeholders and technical teams, ensuring solutions are both business-relevant and technically sound.
Contribute to shaping analytics priorities and roadmaps, balancing near-term delivery with long-term data quality and scalability.