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.