Senior Analytics Engineer I

TL;DR

Senior Analytics Engineer (Data Platform): Building and maintaining data products that support internal analytics and customer-facing features with an accent on complex business logic, data product design and development. Focus on improving the analytics platform and enabling others to do their best data work.

Location: Hybrid role based in our Austin, Chicago, DC, or NYC office. In-office attendance is required on Monday, Tuesday, and Thursday and may increase based on project-based needs and changes to hirify.global’s in-office policy over time.

Salary: $150,000 - $180,000 + equity + benefits.

Company

hirify.global transforms brick-and-mortar commerce by using online retail sophistication to provide users with value on everyday purchases and businesses with new, profitable customers.

What you will do

• Design and build complex, scalable data products that support internal analytics and power product features.
• Translate ambiguous business needs into structured, trustworthy data assets, owning development end-to-end.
• Contribute to shared platform tooling that improves pipeline orchestration, testing, access control, and observability.
• Evaluate and implement new platform capabilities to improve performance, maintainability, and user experience.
• Support and enable teammates across the org by onboarding new users, resolving data issues, and documenting patterns and best practices.
• Maintain reliability and data quality through shield support rotations, strong monitoring, alerting, and test coverage.

Requirements

• 2–4 years of experience working in data or analytics engineering roles, preferably in a modern data stack environment (e.g., Snowflake, dbt, Dagster, Airflow).
• Fluent in SQL, and comfortable writing performant, modular transformations at scale.
• Working knowledge of Python, particularly in the context of data orchestration, transformations, and testing.
• Understand tradeoffs between different modeling approaches and can choose appropriate techniques based on data volume and business needs.
• Can independently communicate technical concepts to both engineers and non-technical stakeholders.
• Think critically about ROI, know when to automate, and understand how to balance long-term quality with short-term delivery.

Nice to have

• Experience supporting machine learning workflows, such as building features or monitoring model inputs and outputs.
• Familiarity with DevOps practices (e.g., CI/CD for data), data governance, or FinOps (cost-conscious design).
• Experience working in a fast-growing startup environment or on platform-style teams that serve internal customers.

Culture & Benefits

• Medical, dental, and vision coverage starting on Day 1.
• Unlimited PTO + 10 paid federal holidays + our annual, week-long Winter Break.
• Equity (ISOs) and 401(k) program.
• Physical fitness and wellness memberships.
• Flexible work environment and lunch reimbursement for in-office employees.