Senior Data Engineer (Spark)

Location: Remote in the United States; available office locations include Austin, Mountain View, New York, Seattle, Washington, DC, and San Francisco. hirify.global can hire in countries where it has a legal entity.

Base pay: $138,942–$181,397 in Zone C, $150,660–$196,695 in Zone B, or $167,400–$218,550 in Zone A, depending on location.

Company

hirify.global develops software products that help teams collaborate and manage different types of work.

What you will do

• Design scalable, high-performance data architecture and solutions that support strategic decision-making.
• Build, launch, and optimize reliable ELT/ETL pipelines for large-scale and specialized datasets.
• Design robust data models optimized for storage, accessibility, and downstream retrieval.
• Implement data quality frameworks covering accuracy, freshness, and trust.
• Partner with engineering, product, program management, leadership, and data science teams to define and prioritize data requirements.
• Own the full data engineering lifecycle, mentor junior engineers, and participate in the on-call rotation.

Requirements

• BE in Computer Science or equivalent and 6+ years of professional experience as a Data Engineer or in a similar role.
• Experience building scalable Spark data pipelines with Airflow or similar scheduling tools.
• Experience with Databricks and its APIs, modern databases, and data lakes.
• Strong programming skills in Python or Scala and advanced SQL skills.
• Experience with CI/CD tools such as Bitbucket and Git, plus large-scale batch and streaming data processing.
• Experience with SaaS companies, machine learning, self-service tooling, or open-source contributions.

Culture & Benefits

• Flexible remote, office, or hybrid work arrangements where legally supported.
• Health and wellbeing resources.
• Paid volunteer days and community-focused benefits.
• Potential eligibility for benefits, bonuses, commissions, and equity.
• Accommodations and adjustments are available throughout the recruitment process.