Senior Data Engineer

Rumble is seeking a Senior Data Engineer to design, build, and operate the data platforms and backend systems that support large-scale product, analytics, and operational workloads. This is a senior individual contributor role for someone who can own systems end to end: you’ll architect them, implement them, help deploy and monitor them in production, and continuously improve performance, reliability, and maintainability as scale and complexity grow.

Our environment centers on large-scale data processing, distributed systems, and backend services that need to perform reliably in production. You should be comfortable working with open data lake formats such as Parquet, data lake catalogs such as Hive, Iceberg, or DuckLake, distributed query and storage systems such as Trino, Doris, Spark, StarRocks, or ClickHouse, and relational databases including MySQL and PostgreSQL. You should also be able to design and implement production APIs at scale, ideally in the JVM ecosystem using Kotlin with Ktor or Quarkus, and work confidently with Redis, event-driven systems, and containerized infrastructure.

You’ll work closely with engineering and product teams to turn ambiguous requirements into clear technical plans, apply strong systems design fundamentals to complex data and backend problems, and make sound implementation decisions across application, data, and infrastructure layers. Day to day, you may design high-performance data pipelines, improve data access patterns across operational and analytical systems, build services that expose and process data reliably, and help raise the engineering bar through strong execution, thoughtful collaboration, and hands-on technical leadership.

Design, build, and operate modern data infrastructure and backend services that support large-scale data processing and product needs. Own systems from architecture through implementation, testing, deployment, monitoring, and ongoing optimization. Develop and improve data pipelines and data lake patterns using open formats and catalog technologies, build APIs and services that expose data reliably at scale, and help ensure performance, resilience, and maintainability across distributed systems. Collaborate with cross-functional partners to solve complex technical problems and raise engineering standards through strong judgment and high-quality execution.

• Strong experience building and operating large-scale data platforms, backend systems, or roles that meaningfully combine both disciplines.
• Strong knowledge of modern data engineering patterns, including data lake style processing with open formats such as Parquet; experience with formats such as Vortex or Lance is a plus.
• Hands-on experience with data lake catalogs and table metadata systems such as Hive, Iceberg, or DuckLake.
• Strong experience with distributed databases, storage engines, or query systems such as Trino, Doris, Spark, StarRocks, or ClickHouse.
• Advanced SQL skills across modern dialects, along with practical experience using relational databases such as MySQL and PostgreSQL.
• Strong backend engineering experience building APIs and services at scale, preferably in the JVM ecosystem using Kotlin with Ktor or Quarkus, or comparable backend technologies.
• Experience with distributed caching systems such as Redis and with queue or event-driven architectures using Kafka, NATS, RabbitMQ, or similar technologies.
• Comfort working in containerized and orchestrated environments, including Kubernetes, and debugging issues across data, application, and infrastructure layers.

• Experience with modern data processing engines such as Polars or DataFusion.
• Experience contributing to the design and improvement of high-scale, distributed systems.
• Experience working directly in Linux-based production environments and operating services in collaboration with platform or infrastructure teams.
• Experience contributing to, maintaining, or working closely with open-source technologies.
• Background in media, streaming, large-scale consumer platforms, or data-intensive product environments.