Engineering Manager (Data Science/ML)

TL;DR

Engineering Manager (Data Science/ML): Leading the Analytics Engineering team and hirify.global’s core data platform with an accent on data ingestion, modeling, and orchestration. Focus on scaling the platform, fostering engineering growth, and driving data accessibility across internal teams.

Location: This hybrid role is 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: $185,000 - $215,000+ equity + benefits. The final starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions.

Company

hirify.global transforms brick-and-mortar commerce using online retail sophistication to provide users more value and businesses new, profitable customers.

What you will do

• Co-create and drive the vision and roadmap for hirify.global’s data platform, shaping the strategy for data in alignment with company goals.
• Ensure the data stack, which includes Snowflake, dbt, and Dagster, enables scalable, self-service, and trustworthy workflows for reporting, analytics, and experimentation.
• Drive strategic data platform initiatives that improve reproducibility, reliability, and analytics enablement.
• Define and track key platform health metrics, including pipeline reliability, SLA adherence, cost-efficiency, and model deployment readiness.
• Partner cross-functionally with Product, Engineering, Data Science, and GTM stakeholders to ensure the data platform supports current and emerging business needs.
• Represent Analytics Engineering in company-wide planning forums, technical councils, and cross-functional working groups.

Requirements

• Have 3+ years of experience managing data, analytics, or ML engineering teams, and at least 3+ years of hands-on experience as an individual contributor building data products, pipelines, or ML systems.
• Are proficient with modern data platforms and tooling, including Snowflake, dbt, and Dagster, and are fluent in core concepts like modeling, orchestration, and data transformation.
• Have hands-on experience or deep familiarity with MLOps practices, such as model versioning, deployment pipelines, monitoring, and reproducibility.
• Thrive in environments where you’re asked to scale teams, elevate systems, and bring clarity to ambiguity.
• Are comfortable designing and reviewing solutions in AWS environments, and make thoughtful tradeoffs to balance performance, cost, and maintainability.
• Communicate clearly across technical and non-technical audiences, and can advocate effectively for platform investments that support long-term business value.

Nice to have

• Are eager to integrate generative AI tools into development workflows to accelerate delivery and improve the developer experience.

Culture & Benefits

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