Analytics Engineer, Go-To-Market Data

Job Description

This role will be based in Sunnyvale, San Francisco, Chicago, or New York. The work location of this role is hybrid, meaning it will be performed both from home and from a Linked In office on select days, as determined by the business needs of the team.

The Analytics Engineer, Marketing Strategy & Technology Data Foundations will support the development and maintenance of scalable data foundations, pipelines, and analytics solutions that enable the Marketing Strategy & Technology organization. You will partner closely with Analytics Engineering, Sales, Strategy & Operations, Engineering, and Data teams to help deliver reliable, high‑quality data products that support critical business workflows and decision‑making.

This is a hands‑on analytics engineering role focused on building, improving, and operating foundational data solutions. You will contribute to the development of data pipelines, curated datasets, monitoring frameworks, and data quality processes that improve the reliability, scalability, and usability of core data assets. Working alongside senior analytics engineers, you will operate within established architectural patterns and engineering standards while continuing to grow your technical and business expertise.

This role is a strong opportunity for someone looking to deepen their experience in analytics engineering within a high‑impact business environment. You will contribute to foundational data systems used across the organization, collaborate directly with cross‑functional partners, and gain exposure to large‑scale data operations and production‑grade data management practices.

The ideal candidate has strong technical fundamentals in SQL, Python, data modeling, and distributed data systems, enjoys solving data and operational challenges, communicates effectively with technical and business stakeholders, and is eager to continuously learn and grow within a collaborative engineering environment.

• Develop and maintain scalable data foundations and solutions including pipelines, datasets, and analytics capabilities that support the Marketing Strategy & Technology organization.
• Deliver well‑defined components of larger data initiatives end‑to‑end, including development, testing, deployment, monitoring, and operational support.
• Contribute to data product reliability, quality, and usability through data quality validation, observability, SLA adherence, and continuous improvement efforts.
• Translate business needs into scalable data solutions by partnering with business and technical stakeholders to gather requirements, validate assumptions, and propose practical implementation approaches.
• Apply engineering and governance standards for data modeling, documentation, testing, governance, and analytics engineering best practices while identifying opportunities to improve maintainability and efficiency.
• Support and evolve existing data products by troubleshooting issues, resolving defects, improving performance, and reducing technical debt within assigned areas.
• Collaborate across teams to deliver solutions through code reviews, design discussions, knowledge sharing, and iterative delivery practices.
• Communicate progress, dependencies, and risks clearly to stakeholders and escalade blockers appropriately to support successful execution.
• Build technical and business domain expertise over time to expand ownership, deepen analytics engineering capabilities, and increase impact.

Basic Qualifications

• Bachelor's degree in Computer Science, Data Science, Information Systems, Statistics, Applied Mathematics, Engineering, Business Analytics, or equivalent practical experience.
• 2+ years of experience in analytics engineering, data engineering, business intelligence, or a closely related data role.
• 2+ years of experience writing production SQL to build, transform, or operate datasets.
• 1+ years of experience with distributed data technologies (e.g., Trino, Presto, Spark SQL) and a workflow orchestrator (e.g., Airflow).
• Working knowledge of data modeling concepts (e.g., dimensional modeling, fact/dim tables, slowly changing dimensions) and data quality fundamentals…