Staff Data Engineer

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  • Company Hauler Hero Inc.
  • Employment Full-time
  • Location 🇺🇸 United States, New York
  • Submitted Posted 4 days ago - Updated 1 hour ago

About Hauler Hero

Hauler Hero is a leading waste management SaaS platform serving haulers and waste management companies. We build modern technology that transforms how the industry operates, from route optimization to billing and customer management.

We’ve achieved product-market fit and are scaling our platform and data infrastructure to support continued growth.

The Role

We’re hiring a Staff Data Engineer to own and scale our customer data migration platform.

This role architects the systems, frameworks, and tooling that power all customer data migrations. You’ll turn complex, manual processes into reliable, repeatable workflows and progressively move migrations from bespoke engineering efforts to standardized, self-service capabilities.

This is a staff-level role focused on leverage. Your work should make the entire team faster and reduce the need for hands-on engineering over time.

What You’ll Do

Migration Platform Architecture & Scalability

  • Own the end-to-end architecture of the data migration platform, from ingestion through validation to production deployment

  • Design migration infrastructure that reduces per-customer engineering effort through reusable components and standardized patterns

  • Build systems that evolve migrations from engineer-led to standardized and self-service

  • Establish automated data quality frameworks including profiling, validation, and anomaly detection

  • Instrument migration systems with dashboards, metrics, and alerts for observability and continuous improvement

Self-Service Tooling & Enablement

  • Architect self-service import workflows for customers and internal teams

  • Design ingestion pipelines that align tightly with Hibernate entity models and lifecycle rules

  • Build intelligent error handling and feedback loops that guide non-technical users through data correction

  • Create Excel-based import templates with embedded validation, documentation, and formatting standards

  • Maintain alignment with application changes by tracking Hibernate model evolution and updating pipelines proactively

Technical Execution

  • Design, build, and maintain ETL pipelines for migrating data from legacy waste management systems

  • Analyze legacy datasets, reverse-engineer business logic, and implement transformation workflows

  • Execute end-to-end customer migrations in coordination with technical and business stakeholders

  • Build tooling to ingest data from legacy LAN-based systems common in the industry

  • Optimize pipeline performance to handle large datasets efficiently and reduce migration timelines

Technical Leadership & Collaboration

  • Drive technical decisions for data infrastructure, including build vs. buy evaluations

  • Partner with Product, Engineering, and Customer Success to shape scalable solutions

  • Mentor team members on data integration and migration patterns

  • Create and maintain documentation, runbooks, and operational guides

  • Lead code reviews and knowledge sharing to raise the bar across the team

What We’re Looking For

Required

  • Advanced SQL expertise including query optimization and schema design

  • Advanced Python proficiency with:

    • SQLAlchemy (ORM and Core)

    • Pandas for transformation and profiling

    • Data validation frameworks (Pydantic or similar)

  • Deep experience with Hibernate or similar ORMs, including lifecycle and performance implications

  • Proven track record building frameworks and platforms, not just pipelines

  • Strong Git and collaborative development practices

  • Experience designing data architectures for transformation, metadata, and orchestration

  • History working with large, disconnected datasets

  • Strong computer science fundamentals and commitment to maintainable code

Preferred

  • Java and Hibernate experience

  • BigQuery

  • Analytics tooling (Sigma, PowerBI, Tableau)

  • Prior experience building self-service data tooling or platforms

What Success Looks Like

  • Ownership mindset: You drive problems to resolution and take accountability for outcomes

  • Systems thinking: You build solutions that make future work easier

  • Bias toward scale: You turn manual processes into automated frameworks

  • Clear communication: You document decisions and prevent information silos

  • Comfort with ambiguity: You make progress with incomplete information and adjust as you learn

  • Continuous improvement: You refactor and extend systems to make them more robust and reusable

Engineering Principles

  • Ownership over tickets

  • Practical over perfect

  • Context over process

  • Customer empathy

  • Build for scale

Compensation & Benefits

  • Competitive salary + equity

  • Health, dental, vision insurance

  • Flexible PTO

  • Remote-first culture

  • Modern development tools and AI assistants

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