Senior Data Platform Engineer

Our client is a global innovator in fitness and wellness technology, delivering integrated experiences to millions of members globally.

Technical Expertise:

• 5+ years in software engineering building and operating production systems.
• Strong backend fundamentals including Python, Java, or Kotlin.
• Experience with large-scale, data-intensive systems.
• Solid understanding of distributed systems concepts like scalability, latency, reliability, and data consistency.
• Experience in cloud environments, preferably AWS.
• Familiarity with relational and NoSQL databases such as PostgreSQL, MySQL, DynamoDB, Redis, and Elasticsearch.

Data Systems & Architecture:

• Hands-on with large-scale data pipelines and processing systems.
• Experience with event-driven architectures and streaming or batch processing using Kafka, Spark, and ETL workflows.
• Understanding end-to-end data flow including ingestion, transformation, storage, and access.
• Designing systems emphasizing data performance, scalability, and reliability.

Collaboration & Engineering Mindset:

• Ability to work cross-functionally to improve system design and data access patterns.
• Strong problem-solving focused on performance, scalability, and reliability.
• Clear communication and collaborative engineering approach.

Data Infrastructure Engineering:

• Design, build, and operate backend systems with scalable, highly available data persistence layers.
• Contribute to architectural decisions for distributed data systems, multi-region persistence, and global scalability.
• Improve reliability and performance of production datastores.

Data Performance & Optimization:

• Partner with teams to improve database schema design, query performance, and data modeling.
• Optimize data access patterns and indexing for relational and NoSQL databases.
• Support systems design for efficient scaling under high load.

Developer Experience & Platform Tooling:

• Build and maintain self-service tooling for provisioning and managing databases and caching layers.
• Contribute to infrastructure automation with tools including Terraform and internal developer platforms.
• Enhance observability and operational insights into datastore performance and reliability.

Platform Reliability & Observability:

• Implement monitoring, metrics, and tracing for production data systems.
• Develop autoscaling and performance optimization strategies.
• Support operational excellence by reducing manual processes and improving system resilience.