Data Engineering Architect (Databricks)

TL;DR

Data Engineering Architect (Databricks/SQL/Python): Designing and operating scalable data pipelines and a Lakehouse architecture to deliver high-quality datasets for analytics and AI with an accent on production-grade data modeling and system reliability. Focus on building Gold layer datasets, optimizing large-scale query performance, and developing reusable engineering frameworks.

Location: Arlington, VA

Company

hirify.global is a leading source of legal, tax, regulatory, government, and business information for professionals.

What you will do

• Design, build, and operate end-to-end scalable data pipelines from ingestion through transformation to serving.
• Evolve the Lakehouse architecture to improve structure, scalability, and consistency across all datasets.
• Build and maintain production-grade, well-modeled Gold layer datasets to power analytics and AI use cases.
• Develop and enforce reusable data engineering patterns and frameworks to reduce duplication.
• Own data quality and reliability for production datasets, including validation, monitoring, and incident resolution.
• Translate business-defined KPIs and ambiguous requirements into scalable, production-ready data solutions.

Requirements

• 8+ years of experience in data engineering with a strong track record of owning production data systems.
• Deep expertise in SQL, including query optimization and performance tuning at scale, and proficiency in Python.
• Hands-on experience with Databricks, Spark, or similar distributed data processing frameworks.
• Strong understanding of Lakehouse architecture, ELT, and layered data models (bronze/silver/gold).
• Experience developing source-controlled pipelines within a CI/CD environment.
• Bachelor’s degree in Computer Science or a related discipline.

Nice to have

• Experience working with product analytics data, such as event tracking, user behavior, and experimentation.
• Master’s degree in Computer Science or a related field.