Senior Software Engineer (Data Platform)

Our leading SaaS-based Global Employment Platform™ enables clients to expand into over 180 countries quickly and efficiently, without the complexities of establishing local entities. At G-P, we’re dedicated to breaking down barriers to global business and creating opportunities for everyone, everywhere.

Our diverse, remote-first teams are essential to our success. We empower our Dream Team members with flexibility and resources, fostering an environment where innovation thrives and every contribution is valued and celebrated.

The work you do here will positively impact lives around the world. We stand by our promise: Opportunity Made Possible. In addition to competitive compensation and benefits, we invite you to join us in expanding your skills and helping to reshape the future of work.

At G-P, we assist organizations in building exceptional global teams in days, not months—streamlining the hiring, onboarding, and management process to unlock growth potential for all.

As a Senior Data Engineer, you will be the architectural backbone of the AI-native Data Platform. You won't just build pipelines; you will design the self-service frameworks and high-performance engines that power every product and AI workflow across the company.

You will bridge the gap between strategic leadership and technical execution, ensuring that our Databricks Lakehouse scales efficiently to handle hundreds of services while maintaining world-class data reliability and cost-efficiency.

• Lead the design and implementation of internal SDKs and self-service frameworks that enable distributed engineering teams to ingest and transform data autonomously.
• Shift from "pipeline building" to "platform engineering," creating reusable patterns for batch and real-time event processing.

• Take full ownership of the cost-effectiveness of the Databricks ecosystem. You will tune Spark execution plans, optimize shuffle partitions, and implement auto-scaling strategies to manage DBU consumption.
• Ensure the platform remains performant as volume grows, managing the trade-offs between latency, throughput, and cloud spend.

• Implement Schema-on-Write validation and Data Contracts to ensure data from hundreds of internal services meets strict quality standards before hitting the Bronze layer.
• Partner with the Data Architect & Data Stewards to enforce data privacy (PII), security standards, and metadata lineage across the global ecosystem.

• Champion the use of AI-assisted development tools (e.g., GitHub Copilot, Cursor) to accelerate the engineering lifecycle and improve code quality.
• Mentor engineers on distributed computing best practices, conducting deep-dive code reviews that focus on scalability and maintainability.

• Scale Specialist: 5+ years of experience building and operating production-grade data systems at massive scale.
• Databricks Expert: Deep, hands-on mastery of the Databricks/Spark ecosystem (Delta Lake, DLT, Spark UI debugging, and performance tuning).
• Streaming Veteran: Proven track record of building Real-time/Streaming architectures (Spark Structured Streaming, Kafka, or Kinesis) as a core production requirement.
• Optimisation Mindset: Experience managing and optimizing cloud costs in a high-growth environment.
• Platform Thinker: Experience building APIs, tools, or frameworks used by other internal engineering teams.