Senior Data Engineer

Jobgether; Canada; On-site; CAD 120,000 - 160,000; Full time

Benefits offered by this job

100% remote within the Americas

One-time USD 500 home-office setup

USD 150 monthly stipend

Job summary

Jobgether is partnering with a company in Canada to recruit a Senior Data Engineer for a fully remote, globally distributed team. You will design, build, and operate large-scale data platform infrastructure, including lakehouse, streaming, and batch processes, using open-source, cloud-native tools.

You’ll collaborate with DevOps and Analytics teams while owning critical data infrastructure. The role emphasizes scalable architecture, reliability, and governance, with opportunities to work on

Qualifications

• 5+ years of professional experience in Data Engineering, including building and operating scalable data platforms.
• Strong hands-on experience operating data infrastructure on Kubernetes with cloud-native technologies.
• Experience with infrastructure as code and deployment automation (Terraform, Ansible, ArgoCD).
• Deep understanding of distributed systems and open-source query engines (Trino/Presto).
• Experience with Apache Iceberg, object storage, and open table formats.
• Proficiency with streaming (Kafka/Redpanda/Debezium) and ELT tools (Airflow, Airbyte).
• Strong Python and SQL skills for building production pipelines and tooling.
• Experience with Google Cloud Platform and data services (GCS, Cloud Build, Cloud SQL, Dataproc).
• Ability to thrive in a fast-paced startup environment and drive pragmatic technical decisions.
• Familiarity with semantic/metrics layers (Cube, dbt, Looker) is a plus.

Responsibilities

• Design, build, and evolve core data platform infrastructure and lakehouse components.
• Own lakehouse infrastructure as code and deployments (Terraform, Ansible, ArgoCD).
• Build streaming and batch data ingestion pipelines handling hundreds of millions of events daily.
• Develop scalable data ingestion/processing to support large-scale analytics and AI interfaces.
• Enhance BI landscape so downstream teams and AI agents access lakehouse data efficiently.
• Establish platform reliability: monitoring, alerts, on-call, incident response, runbooks.

Skills

Python

SQL

Kubernetes

Docker

Helm

Terraform

Ansible

ArgoCD

Airflow

Airbyte

dbt

Trino/Presto

Apache Iceberg

Kafka

Redpanda

Debezium

OpenMetadata

DataHub

Cloud Platform (GCP/AWS)

Dataproc

GCS

Cloud Build

Looker

Tools

Kubernetes

Docker

Helm

Terraform

Ansible

ArgoCD

Airflow

Airbyte

dbt

OpenMetadata

DataHub

Apache Iceberg

Kafka

Redpanda

Debezium

Trino/Presto

Iceberg

GCS

Dataproc

Cloud Build

Job description

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‑powered 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!