Senior Data Engineer, Event Data

Location: REMOTE / Toronto, Ontario
This job allows you to work remotely.

We're working with an established technology company that is building out a new Event Data team and looking for a Senior Data Engineer to help design and develop the systems responsible for ingesting, processing, and serving extremely large volumes of event data.
This is a hands-on senior engineering role focused on large-scale streaming and event-driven data systems. You'll work across event ingestion, stream processing, analytical data stores, data modeling, system migrations, and production operations while helping establish the architecture and engineering practices for a newly formed team.
The environment operates at billions-of-events scale, making this a strong opportunity for an engineer who has worked with high-volume streaming systems and enjoys solving complex data infrastructure problems.
What You'll Do
- Design, build, and maintain high-volume event streaming pipelines that ingest data from external systems, internal services, and third-party sources.
- Build systems capable of reliably processing and serving event data at billions-of-events scale.
- Develop and operate analytical databases and data models optimized for high-volume queries and low-latency access.
- Build production services and pipeline components using Python and/or Elixir.
- Work with stream-processing technologies and message brokers such as Apache Flink, Kafka, Pulsar, or Kinesis.
- Integrate existing event pipelines with modern streaming infrastructure and design migration strategies that minimize disruption to downstream systems.
- Build monitoring, alerting, and observability around event pipelines, including data freshness, pipeline health, SLAs, and production reliability.
- Define and maintain event schemas, data contracts, and data-quality standards.
- Collaborate with data platform, product engineering, analytics, and other technical teams that produce or consume event data.
- Participate in architecture and system-design discussions while helping establish engineering standards and best practices for the team.
- Help operationalize the platform through infrastructure automation, documentation, runbooks, and production support.

Must Have Skills:
What We're Looking For
- 6+ years of professional experience in data engineering, backend engineering, systems engineering, or a closely related area.
- Significant hands-on experience building event-driven, streaming, or high-volume data systems.
- Strong proficiency with Python and/or Elixir for building production services, application connectors, data-processing components, or pipelines.
- Advanced SQL skills, including data modeling, query optimization, and analytical workloads.
- Production experience with columnar or OLAP databases and large analytical datasets.
- Strong experience with streaming technologies and message brokers such as Apache Flink, Kafka, Pulsar, or Kinesis.
- Experience designing systems that process large volumes of events or messages with requirements around throughput, latency, reliability, and availability.
- Experience migrating or integrating legacy systems with newer distributed or streaming architectures.
- Strong production engineering experience, including monitoring, alerting, observability, SLA/SLO management, dashboards, and runbooks.
- Experience designing and operating cloud-based data infrastructure, particularly on AWS. GCP experience is also valuable.
- Experience with Infrastructure-as-Code tooling such as Terraform or CloudFormation.
- Ability to contribute meaningfully to system design and architecture discussions and communicate technical trade-offs clearly across engineering teams.

Nice to Have Skills:
Nice to Have
- Hands-on Apache Flink experience.
- Experience with Databricks.
- Experience with multiple messaging or streaming platforms such as Kafka, Pulsar, or Kinesis.
- Experience working with retail, ecommerce, marketing, clickstream, purchase, or product-interaction event data.
- Experience building data platforms where event data needs to be consumed by multiple downstream products, analytics systems, or engineering teams.
- Experience working with data systems operating at billions-of-events scale.