Analytics Platform Engineer

PepsiCo•New York, NY

23h•Onsite

About The Position

The Digital Commerce Analytics Platform team builds, maintains, scales, and evolves the analytics platform that powers data engineering, analytics engineering, and report/insight delivery across PepsiCo's Digital Commerce organization. This entry-level role will contribute to platform development — from dbt project infrastructure and CI/CD to orchestration, metadata systems, and emerging AI-driven capabilities. The Analytics Platform Engineer will work alongside senior engineers to deliver reliable, scalable platform tooling that delivery engineering teams depend on daily.

Requirements

1–2 years of experience (including internships, co-ops, or substantial academic/personal projects)
Familiarity with dbt or similar transformation frameworks
Familiarity with Apache Airflow or similar orchestration tools
Experience with CI/CD systems (GitHub Actions, Jenkins, or equivalent)
Exposure to BI platforms (Power BI, Tableau, or equivalent)
Exposure to large language models and their application to analytics workflows
Experience with version control (Git) and branching strategies
Familiarity with cloud data platforms (Snowflake, BigQuery, or Databricks)

Responsibilities

Develop and maintain the dbt project infrastructure — including CI/CD workflows, project configuration, macros, and testing frameworks — that delivery engineering teams build upon
Build and support Apache Airflow orchestration infrastructure, ensuring reliable scheduling, monitoring, and scalability for data pipelines
Maintain and evolve the CI/CD platform (GitHub Actions workflows, pre-commit hooks, automated validation) that enforces quality and deployment standards
Manage and administer the Power BI platform — including workspace governance, capacity management, and deployment pipelines — to support report delivery teams
Develop and maintain metadata collection systems for tracking pipeline health, lineage, and model ownership
Define and enforce best practices, patterns, and conventions across data delivery and report delivery engineering teams
Support evaluation and prototyping of AI-for-BI capabilities, including automated insight generation using large language models
Research and evaluate emerging tools and technologies; present findings and recommendations to inform platform direction
Participate in build-vs-buy assessments and contribute to tooling decisions that delivery engineering teams depend on
Collaborate with engineering teams to identify platform gaps, gather requirements, and deliver scalable solutions