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Data Platform Engineer – SeniorKey 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 rigorPosition OverviewWe are seeking aSenior Data Platform Engineerto 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 ResponsibilitiesData Platform Architecture & EngineeringArchitect highly scalable data storage, processing, and transformation systemsLead development of Iceberg-based lakehouse components and metadata systemsDesign and optimize distributed ETL and streaming pipelines using Spark and KafkaBuild backend services in Python, Go, Scala, or Java to support platform capabilitiesImprove Trino performance, query federation, and metadata scalabilityExperimentation Platform LeadershipDevelop and champion tools, frameworks, and processes that drive experiment validityPartner with executives, product, and data science on experiment design and analytics strategyBuild automation that increases rigor and efficiency across experimentation workflowsData Governance, Security & ReliabilityOwn RBAC strategy, IAM standards, and enterprise data access patternsLead observability initiatives using Datadog for logs, alerts, and distributed performance metricsEnsure reliability, reproducibility, and schema governance across all data assetsGuide best practices for metadata integrity and data lifecycle managementInfrastructure & DeploymentLead Kubernetes and Helm deployment patterns for data platform servicesBuild automation that improves operational efficiency and reduces platform frictionDrive capacity planning, scaling strategies, and performance tuning for large data systemsTechnical Leadership & MentorshipInfluence platform roadmap and long-term architectural decisionsMentor engineers across the team and support onboarding to platform systemsWrite clear documentation and contribute to engineering playbooksQualificationsDegree in Computer Science, Engineering, or related field5+ yearsin data platform, distributed systems, or backend platform engineeringAdvanced proficiency inPython,Go,Scala, orJavaDeep experience withIceberg-based data warehousesand lakehouse architecturesExpert-level knowledge ofSpark,Kafka,Trino, and distributed querying systemsStrong experience with RBAC design, IAM security, and data governance toolingKubernetes, Helm, and CI/CD experience supporting production environmentsStrong architectural thinking, systems design, and cross-team leadership skillsWhy Join UsThis 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.
