Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading global technology company is seeking a Platform & Data Engineer to support and evolve the systems relied upon by thousands of internal engineers. This role sits at the intersection of Kubernetes platform engineering and large-scale data engineering, combining ownership of critical compute infrastructure with responsibility for the data platforms that power internal tools and services. The organization fosters innovation, collaboration, and technical excellence, providing opportunities to build solutions that create company-wide impact.
The organization is looking for a Platform & Data Engineer to serve as a key contributor within a highly collaborative team responsible for internal automation and testing infrastructure. This role offers broad ownership across platform and data domains, including Kubernetes operations, observability, API development, database optimization, ETL pipelines, and self-service data tooling.
The successful candidate will thrive in an environment that values ownership, technical depth, and product thinking. This position requires balancing infrastructure scalability, performance optimization, and user-focused tooling to enable internal teams to move faster and operate more effectively.
Key Responsibilities
- Lead the scalability, reliability, and debuggability of Kubernetes infrastructure operating at large scale.
- Take ownership of observability systems, including error tracking, monitoring, and log aggregation across services.
- Design and develop internal APIs with considerations for versioning, multi-tenancy, authentication, and capacity planning.
- Collaborate with engineering teams to improve platform adoption and enhance self-service capabilities.
- Manage and optimize large-scale MongoDB environments handling millions of records daily.
- Define indexing, query optimization, and sharding strategies to support growing datasets and application performance.
- Own and enhance ETL pipelines responsible for data ingestion, transformation, and reliability.
- Design and deliver self-service query tools that empower internal teams to access and analyze data independently.
- Build user-facing data products and interfaces with a focus on usability, performance, and scalability.
- Explore and implement modern approaches such as query builders, query templates, and AI-assisted query construction.
- Partner with stakeholders to evolve platform capabilities from curated solutions to scalable self-service experiences.
- Drive sustainable operational practices through automation, monitoring, and system instrumentation.
Required Qualifications
- Bachelor’s degree in Computer Science, a related field, or equivalent practical experience.
- 3–5 years of professional software engineering experience, including hands-on production experience with Kubernetes, large-scale data systems, or internal platform infrastructure.
- Deep experience operating Kubernetes environments at scale, including debugging, performance optimization, and cluster management.
- Experience working with MongoDB or similar document databases, including aggregation patterns and performance optimization techniques.
- Strong proficiency in Python and experience supporting production applications and services.
- Experience building and operating solutions within large-scale infrastructure environments.
- Strong problem-solving, analytical, and communication skills.
- Ability to manage complex technical challenges while balancing business and user needs.
Preferred Qualifications
- Experience with production observability platforms, including monitoring, error tracking, and log management.
- Knowledge of API design and development, particularly in areas such as authentication, multi-tenancy, versioning, and scalability.
- Experience building and maintaining ETL and data pipeline solutions.
- Strong product mindset with a track record of developing tools that drive user adoption.
- Experience building self-service data products, query builders, or analytical interfaces.
- Exposure to AI-assisted query generation, LLM-powered tooling, or Model Context Protocol (MCP)-based solutions.
- Experience supporting platform evolution toward scalable self-service models.
- Commitment to operational excellence through automation, observability, and sustainable engineering practices.
- Comfortable working in small, highly collaborative teams with broad ownership responsibilities.
Disclaimer
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