Senior Data Analytics Engineer (SaaS)

TL;DR

Senior Data Analytics Engineer (SaaS): Building and maintaining business-critical data systems to drive performance visibility across sales, marketing, and product with an accent on data engineering and system scalability. Focus on designing ETL pipelines, integrating production data with business tools, and leveraging GCP to empower data-driven decision-making.

Location: Must be based in the US (CA, CO, MA, NY, NJ, OR, PA, TX, UT, WA), UK, Singapore, or Canada.

Salary: $170,000–$190,000 (NY/CA base)

Company

hirify.global is a leading omnichannel customer engagement platform powering personalized messaging for over 1.5 billion monthly active users.

What you will do

• Design and maintain distributed data systems to provide reliable, real-time data for internal stakeholders.
• Connect production backend data to business systems like Salesforce, Marketo, and Intercom.
• Build and optimize ETL pipelines and centralize sources of truth within a data warehouse/lake.
• Innovate data infrastructure using GCP to scale with business requirements.
• Analyze data to generate business insights and build machine learning models for churn prediction and upsell opportunities.
• Collaborate cross-functionally with engineering, operations, finance, and product teams.

Requirements

• 6+ years of professional experience in a technical role at a high-growth startup.
• Must be located in the US (specific states), UK, Singapore, or Canada.
• Proficiency with Python and experience with DBT and Airflow.
• Strong understanding of SaaS metrics and infrastructure scaling challenges.
• Ability to work cross-functionally with both technical and business teams.
• Self-driven mindset with a focus on identifying and implementing data solutions.

Culture & Benefits

• Remote-first work environment with flexible office options in NY and other locations.
• Competitive equity program and comprehensive benefits package.
• Emphasis on ownership, personal growth, and collaborative team culture.
• Focus on high-scale data challenges and innovative technology stacks.