Lead Data Engineer (GCP)

TL;DR

Lead Data Engineer (GCP/Spark): Designing and developing complex ETL processes for an enterprise information warehouse migration to GCP with an accent on scalable data pipelines and high-availability architectures. Focus on optimizing data integration, overseeing data modeling, and implementing large-scale data processing frameworks.

Location: Chandler, AZ (Hybrid)

Company

hirify.global is a staffing firm providing specialized talent for enterprise technology initiatives.

What you will do

• Lead initiatives for large-scale data processing frameworks and enterprise strategy deliverables.
• Design, develop, and maintain optimized, high-availability data pipelines to facilitate deep analysis and reporting.
• Oversee data integration work, including data modeling and maintaining the warehouse and analytics environment.
• Analyze and resolve complex technical challenges related to data engineering, policies, and compliance.
• Collaborate with cross-functional managers and colleagues to achieve strategic data goals.
• Perform code reviews with a strong focus on testability and code coverage.

Requirements

• 6+ years of experience in Data Engineering.
• Strong expertise in Ab Initio, Spark Core, Spark SQL, Data Frames, and Datasets.
• 3+ years of experience with Python or other scripting languages.
• Hands-on experience with GCP (or other cloud platforms like AWS/Azure).
• 3+ years of data warehouse and SQL experience (Teradata, PL/SQL, Oracle).
• Must be eligible to work on a W-2 basis only (No C2C or 1099).

Nice to have

• Prior experience leading teams and managing major programs.
• Knowledge of MongoDB and real-time data streaming via Kafka.
• Experience developing end-to-end ML/AI pipelines, including LLMs and Agentic AI frameworks (e.g., Vertex AI, NLP).
• Familiarity with CI/CD pipelines, infrastructure as code, and cloud deployment best practices.

Culture & Benefits

• Hybrid work schedule in Chandler, AZ.
• Opportunity to work on high-impact GCP migration projects.
• Collaborative environment within the Data & Insights team.