Tech Stack

Description

You will lead high-value, cross-functional analytics initiatives, collaborate with leadership to design analytics processes, and build the tools and infrastructure behind our data platform. You will explore big data from diverse sources, build dashboards, and conduct in-depth analysis to inform product and business decisions.

Requirements

• Bachelor's degree in a technical or math-based discipline, or equivalent practical experience.
• Minimum of 7 years of experience in data analytics or engineering.
• Expert-level SQL knowledge, with proficiency in large-scale data manipulation and performance optimization.
• Experience leading cross-functional projects from ideation through execution.
• Developed experience with data architecture, pipelines, and ETL processes.
• Background spanning at least two of: data engineering, analytics engineering, and business/product analysis.
• Strong attention to detail and commitment to data accuracy.
• Excellent communication skills — able to convey technical concepts clearly to non-technical stakeholders and executives.
• Customer-centric mindset.
• Critical thinking and structured problem-solving.

Responsibilities

• Collaborate with leadership to design state-of-the-art analytics processes for your team.
• Manage cross-functional projects, including defining success criteria, recruiting stakeholders, and clarifying steps for success.
• Conceptualize and build the tools and infrastructure behind our data platform and analytics.
• Explore and analyze big data from a variety of sources; first party, third party and product usage.
• Build and iterate on dashboards that inform stakeholders across product, engineering, sales and customer support teams.
• Conduct in-depth analysis that tell the story of how our products are being used and identify improvements.
• Design and instrument KPIs with product managers, product leaders, engineers and other data analysts.
• Create compelling presentations with actionable recommendations.
• Share insights across the team and company, up to the executive staff.
• Build robust multi aggregation data transformation pipelines that process billions of records across thousands of customers.
• Ensure that these pipelines operate under a defined engineering framework.