Senior or Staff Software Engineer, Data Platform (Canada)

Role overview

A data platform team is hiring a senior or staff-level software engineer to help design and operate the infrastructure behind blockchain intelligence products used by public- and private-sector investigators. The work centers on highly available, petabyte-scale systems that ingest activity from many blockchains and expose it through low-latency query services. The position partners closely with data scientists, backend engineers, and product managers, and the team emphasizes shipping in weeks rather than months while maintaining operational rigor.

Responsibilities

- Design, build, and operate reliable data services that integrate with dozens of blockchain networks and feed downstream product capabilities. - Develop and maintain complex ETL pipelines that transform and process petabytes of structured and unstructured data in real time. - Model data for optimal storage and retrieval, targeting sub-second query latency over blockchain transaction activity. - Deploy, monitor, and scale large database clusters with a sustained focus on performance, availability, and cost efficiency. - Collaborate across data science, backend engineering, and product to design novel data models that improve analytics, investigation, and case-building workflows. - Identify and ship "skateboard" versions of internal tools — such as self-serve automation, observability dashboards, and fast deployment milestones — to compress delivery timelines and reduce single-point-of-failure dependencies.

Requirements

- Bachelor's degree (or equivalent) in computer science or a related field. - 5+ years of hands-on experience architecting distributed systems and guiding projects from initial ideation through successful production deployment. - Strong programming skills in Python, plus working proficiency in SQL or SparkSQL. - Deep familiarity with at least one of the following: data stores and engines such as Iceberg, Trino, BigQuery, StarRocks, or Citus; pipeline and workflow orchestration tools like Airflow or DBT; or streaming and processing frameworks such as Spark, Kafka, and Flink. - Experience deploying and monitoring infrastructure on public cloud platforms using tools such as Docker, Terraform, Kubernetes, and Datadog. - Demonstrated ability to load, query, and transform very large production datasets.

Nice to have

- A bias toward 80/20 thinking: ability to separate must-haves from nice-to-haves when bringing a new environment online, and a preference for shipping in weeks rather than months. - Comfort with self-service automation patterns such as provisioning pgbouncer instances, scaling disks, or upgrading clusters without manual intervention. - Strong judgment, slope (learning velocity), and a track record of building quickly without sacrificing rigor.