Senior Software Engineer (Data Platform)

About the Role

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.

What You Will Do

Architect the Data Platform

• 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.

Own Platform Performance

• 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.

Drive Data Contracts & Governance

• 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.

Lead an AI-Infused SDLC

• 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.

What We Are Looking For

• 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.

Our ranges are established using objective market benchmarking data for this level of work. Final placement within the range is determined by gender-neutral criteria, including the candidate's relevant skills, experience, and specific qualifications for the role.

Actual compensation for this position may vary and will depend on multiple factors such as relevant qualifications, experience, education, and geographic location. For Full-Time Regular Employees, this position is also eligible for additional compensation as follows:

Sales Roles: This position is eligible for a commission structure in addition to base salary.

Non-Sales Roles: This position is eligible for an annual bonus which is paid dependent on various factors, including and without limitation, individual and company performance in addition to base salary.