This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Engineer based in Canada.
Join a fully remote, globally distributed data engineering team building the next generation of a high-scale data platform.
You’ll work with hundreds of millions of events each day across financial transactions, customer data, APIs, system metrics, and third-party sources.
The role combines platform engineering, streaming, batch processing, lakehouse architecture, and business intelligence enablement.
You’ll design and operate infrastructure that supports analytics, reporting, AI-driven interfaces, and external data consumers.
The environment emphasizes open-source technologies, cloud-native engineering, automation, reliability, and scalable architecture.
You’ll collaborate closely with DevOps, Analytics Engineering, and other technical stakeholders to evolve the platform as the business expands.
This is an opportunity to have meaningful ownership over critical data infrastructure in a fast-moving, international technology environment.
Accountabilities
- Design, build, and continuously evolve core data platform infrastructure, including distributed query engines, orchestration, warehousing, cataloging, and related platform capabilities.
- Own lakehouse infrastructure as code and manage deployments using Terraform and Ansible across Kubernetes-based environments.
- Build and maintain low-latency streaming and change-data-capture pipelines, as well as batch ingestion workflows landing data in Apache Iceberg.
- Develop scalable, reliable data ingestion and processing solutions capable of supporting hundreds of millions of events per day.
- Expand and optimize the business intelligence landscape so downstream teams and AI agents can access lakehouse data efficiently and independently.
- Establish and maintain platform reliability practices, including monitoring, alerting, on-call processes, incident response, maintenance windows, runbooks, and service-level objectives.
- Partner with DevOps, Analytics Engineering, and other stakeholders to identify infrastructure gaps and deliver solutions for evolving data requirements.
- Contribute to data experimentation, cataloging, governance, and monitoring capabilities across the platform.
- Use Python and SQL to develop data pipelines, automation, platform tooling, and supporting services.
- Evaluate architectural and technology choices with consideration for scalability, performance, reliability, maintainability, and operational cost.
Requirements
- 5+ years of professional experience in Data Engineering, including at least 2 years building and operating scalable, low-latency data platforms processing more than 100 million events per day.
- Strong hands-on experience operating data infrastructure on Kubernetes, with cloud-native technologies such as Docker and Helm.
- Production experience with infrastructure as code and deployment automation using Terraform, Ansible, ArgoCD, or equivalent technologies.
- Deep understanding of distributed systems, including storage, transactions, and query processing, with hands-on experience operating open-source query engines such as Trino or Presto.
- Strong experience with object storage and open table formats, particularly Apache Iceberg.
- Proven experience with streaming and CDC technologies such as Kafka, Redpanda, and Debezium.
- Hands-on experience with orchestration and ELT tooling, particularly Airflow and Airbyte.
- Strong Python and SQL skills for building production-grade pipelines and platform tooling.
- Experience with Google Cloud Platform and data services such as GCS, Cloud Build, Cloud SQL, and Dataproc, or comparable experience with other major cloud platforms.
- Ability to operate effectively in a fast-paced startup environment, adapt to rapidly changing requirements, and make pragmatic technical decisions.
- Strong problem-solving, communication, collaboration, and ownership skills.
- Familiarity with semantic and metrics layers such as Cube, dbt, or Looker is a plus.
- Experience with dbt, Hightouch, OpenMetadata, DataHub, Apache Ranger, or similar transformation, reverse ETL, cataloging, lineage, and governance tools is advantageous.
Benefits
- Competitive salary and stock options.
- Health benefits.
- 100% remote work within the Americas.
- One-time USD $500 home-office setup allowance for new hires.
- USD $150 monthly stipend provided through a Brex card.
- Opportunity to work with a globally distributed team of engineers and data professionals.
- Exposure to high-scale data infrastructure supporting financial services and technology products.
- Opportunity to work extensively with open-source technologies and modern cloud-native data architecture.
- Collaborative environment focused on curiosity, empathy, accountability, and continuous growth.
- Meaningful ownership over critical data platform infrastructure and its evolution.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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