Senior Data Platform Engineer

Data Platform Engineer – Senior Key Highlights Lead the design and expansion of large-scale data platform components supporting analytics, experimentation, and machine learning workloads Architect and optimize ETL, streaming, metadata, and federated querying systems at scale Drive performance tuning, observability, and reliability efforts across distributed data environments Own RBAC strategy, data governance patterns, and secure access controls for enterprise-wide users Build and evangelize platform-level tooling that accelerates experimentation and raises analytical rigor Position Overview We are seeking a Senior Data Platform Engineer to architect, build, and scale a next-generation data platform. This is a high-impact role responsible for designing distributed systems, improving core platform capabilities, and guiding engineering teams in building reliable and performant data workflows. You will lead initiatives across storage, streaming, querying, metadata, and experimentation, while influencing long-term platform direction and engineering standards. Key Responsibilities Data Platform Architecture & Engineering Architect highly scalable data storage, processing, and transformation systems Lead development of Iceberg-based lakehouse components and metadata systems Design and optimize distributed ETL and streaming pipelines using Spark and Kafka Build backend services in Python, Go, Scala, or Java to support platform capabilities Improve Trino performance, query federation, and metadata scalability Experimentation Platform Leadership Develop and champion tools, frameworks, and processes that drive experiment validity Partner with executives, product, and data science on experiment design and analytics strategy Build automation that increases rigor and efficiency across experimentation workflows Data Governance, Security & Reliability Own RBAC strategy, IAM standards, and enterprise data access patterns Lead observability initiatives using Datadog for logs, alerts, and distributed performance metrics Ensure reliability, reproducibility, and schema governance across all data assets Guide best practices for metadata integrity and data lifecycle management Infrastructure & Deployment Lead Kubernetes and Helm deployment patterns for data platform services Build automation that improves operational efficiency and reduces platform friction Drive capacity planning, scaling strategies, and performance tuning for large data systems Technical Leadership & Mentorship Influence platform roadmap and long-term architectural decisions Mentor engineers across the team and support onboarding to platform systems Write clear documentation and contribute to engineering playbooks Qualifications Degree in Computer Science, Engineering, or related field 5+ years in data platform, distributed systems, or backend platform engineering Advanced proficiency in Python, Go, Scala, or Java Deep experience with Iceberg-based data warehouses and lakehouse architectures Expert-level knowledge of Spark, Kafka, Trino, and distributed querying systems Strong experience with RBAC design, IAM security, and data governance tooling Kubernetes, Helm, and CI/CD experience supporting production environments Strong architectural thinking, systems design, and cross-team leadership skills Why Join Us This is an opportunity to shape the future of a large-scale data platform used across the organization. You'll influence architecture, lead high-impact initiatives, and help build systems that power experimentation, analytics, and innovation at scale.