Senior Data Platform Engineer

• Design, build, and operate scalable, high-performance data infrastructure for batch and real-time processing
• Drive vague or high-level problem statements to concrete architectures and production deployments end to end
• Define and manage platform infrastructure as code
• Build self-healing and observable systems requiring minimal manual intervention
• Build tooling, SDKs, and self-service paths for Data Engineers, Software Engineers, and AI agents
• Implement optimized data pipeline architectures and data access patterns for global teams
• Collaborate with Software Engineers, Data Engineers, Product, Marketing, and other departments
• Participate in design reviews and provide technical direction
• Integrate cloud security and access-control best practices across distributed data assets
• Troubleshoot and resolve performance bottlenecks in distributed data systems
• Build diagnostic and monitoring tools to identify system-health and data-availability issues
• Optimize cloud costs and infrastructure performance
• Maintain and improve platform components ingesting and querying tens of trillions of data points
• Participate in peer code reviews and technical documentation
• Establish CI/CD pipelines and proactive alerting mechanisms
• Report directly to the VP of Data and Architecture
• Independently design and execute platform features and contribute to operational processes

• 4+ years of experience in data platform engineering, data infrastructure, or backend software development
• Ability to take vague or high-level requirements and drive them to concrete, production-ready solutions
• Strong proficiency in performant, concurrent data services and platform tooling
• Deep expertise in Go, Java, Python, Ruby, or Scala;
  Go preferred
• Hands-on experience designing and operating modern data stacks involving Apache Iceberg, Glue catalog, S3, Trino, Spark, Click House, and Kafka
• Experience operating data stacks within Kubernetes
• Proficiency with Dev Ops and Site Reliability Engineering principles
• Proficiency defining infrastructure with Terraform or Cloud Formation
• Experience creating self-healing systems and observability solutions using Prometheus, Grafana, or Datadog
• Ability to manage and tune Postgres/RDS and distributed systems
• Knowledge of data consistency, efficient indexing, and query performance optimization
• Ability to expose data and infrastructure through catalogs, metadata, semantic layers, MCP servers, and agent-safe query paths
• Experience using AI coding tools
• Ability to articulate architectural trade-offs and defend design choices with data
• Ability to professionally challenge the status quo
• Production Go experience, Spark-on-Kubernetes operations, CDC-based ingestion, data catalog and lineage tooling, Snowflake or Databricks, and open-source data tooling contributions are strong nice-to-haves

Demonstrates expertise in designing and operating scalable data infrastructure, with a strong focus on performance optimization, observability, and self-healing systems. Proficient in leveraging modern data stacks and cloud technologies to deliver production-ready solutions.

• Data Platform Engineering
• Go Programming
• Apache Spark
• Terraform
• Kubernetes

• Data Infrastructure
• Backend Software Development
• Concurrent Data Services
• Performance Optimization
• Data Consistency
• Query Performance
• Self-Healing Systems
• Observability Solutions
• Data Cataloging
• AI Coding Tools

• Problem Solving
• Collaboration
• Technical Direction
• Challenging Status Quo
• Articulating Architectural Trade-offs

• Dev Ops
• Site Reliability Engineering
• Infrastructure as Code
• CI/CD Pipelines
• Distributed Systems

• Apache Iceberg
• AWS Glue
• Amazon S3
• Trino
• Click House
• Kafka
• Prometheus
• Grafana
• Datadog
• Postgres