Data Platform & Analytics Engineer

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. 

Responsibilities

• 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, OpenMetadata, 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 escalate 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.

Qualifications

• 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/ClickHouse, 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 storage.

• Proven experience building derived layers (bronze/silver/gold, marts, or similar patterns) for BI and analytics, ideally with dbt or a comparable transformation framework.

• Comfort working with evolving and sometimes loosely defined data requirements, and turning them into stable, well-documented tables and views that are easy to use and maintain.

• Practical experience with Kubernetes, Helm, and containerized deployments—sufficient to safely install, upgrade, and troubleshoot data tools without compromising data integrity.

• Strong understanding of data durability and safety practices (backups, versioning, replication, restore drills) in cloud storage and database environments.

• Ability to collaborate with analysts and non-technical stakeholders, asking the right questions to design data structures that match how they actually query and use the data.

Preferred Qualifications

• Experience with OpenMetadata or a comparable data catalog (DataHub, Amundsen, Atlan).

• Familiarity with dbt and modern data-modeling practices.

• Exposure to streaming systems (Kafka, Pub/Sub) for near-real-time analytics.

• Background in ad-tech, brand safety, or content moderation domains.

• Experience with FinOps practices and cloud cost optimization for analytics workloads.

Annual Compensation Range: 

$100,000 - $120,000 CAD base + benefits + equity

Note: The salary range listed for this position is a good faith estimate based on experience, qualifications, and internal compensation structure. The actual salary offered varies depending on the candidate's skill level and experience. This posting refers to an active vacancy within the organization.