Analytics Platform & Reliability Engineer

Rumble is the Freedom-First technology platform. We proudly offer a video platform, cloud services, advertising solutions, and a non-custodial cryptocurrency wallet.

Rumble is building the analytics and data platform that powers executive decision-making, operational intelligence, and product insights across the company. As we scale our data infrastructure and expand self‑service analytics, we’re looking for an engineer who is as comfortable working in SQL and Parquet as they are operating in Kubernetes.

As a Data Platform & Analytics Engineer, you’ll spend most of your time working directly with data: shaping warehouse schemas, building derived and “gold” layers for BI, and turning complex real‑world datasets into fast, reliable, analytics‑ready tables in Apache Doris, Trino, and related systems. You’ll still handle installs and upgrades for tools like Superset and n8n, but the core impact of this role is in how you model, move, and safeguard data.

You’ll partner across teams, working with data engineering on warehouse architecture and data contracts, Rumble Ad Center (RAC) on ad‑ops and search‑related use cases, and Rumble engineering on system interoperability. This isn’t just operational work: you’ll design data models, determine how new datasets are brought into Doris and the data lake, build views and materialized views that make BI workflows seamless, and make careful, informed decisions that keep our data accurate, performant, and recoverable as priorities evolve.

• Design and build derived and “gold” data layers in Doris and Trino that power Superset dashboards and other BI tools, using views, materialized views, and well‑structured warehouse schemas.
• Translate high‑level, often ambiguous data requests into clear table designs, partitioning strategies, and query patterns that support the analyses teams need to perform.
• Ingest and normalize messy, inconsistent, or sparsely documented data from multiple sources (files, blob stores, APIs), handling file formats such as Parquet, JSON, CSV, and Hive/Iceberg‑style tables safely and predictably.
• Implement and maintain safe data operations: understand how different engines handle DDL/DML operations and table types; configure storage, versioning, and cross‑region protection so that errors are detectable and recoverable rather than catastrophic.
• Build and optimize SQL across multiple engines (Doris, Trino/Presto, DuckDB, and others), including performance tuning, explain‑plan analysis, and making schema or modeling changes to support efficient querying.
• Collaborate with RAC and search‑focused teams on use cases such as identifying searches with no matching content, including solutions that leverage embeddings and vector‑based approaches when appropriate.
• Support analysts and product teams by iterating quickly on table structures, metrics, and data contracts, then documenting how to use those assets effectively in Superset and other analytics tools.
• Own a smaller but critical slice of platform administration: install and upgrade Superset, Doris, Trino, DuckDB, Open Metadata, and n8n on Kubernetes/VMs using Helm, and troubleshoot configuration issues when they arise.
• Set up and maintain SLOs and monitoring for query performance, job health, and data quality, and drive or elevate remediation when platform issues impact analytic workflows.
• Act as a safeguard for the data platform by reviewing and validating changes that affect schemas, storage, or critical datasets, and by using automation and AI tools thoughtfully rather than relying on them for high‑risk operations.

• 4+ years of experience in data engineering, analytics engineering, or data platform roles, with a strong focus on SQL, data modeling, and database behavior rather than only BI or dashboarding.
• Deep SQL experience across multiple engines (e.g., Trino/Presto, Doris/Click House, Postgres, MySQL), including schema design, complex joins, and performance optimization at scale.
• Hands‑on experience with data lakes and table formats (Parquet, Hive, Iceberg or similar), and an understanding of how operations such as deletes, compaction, and merges affect underlying files and…