About the role

Senior Data Engineer developing and managing Flinks' data platform to power their financial intelligence products. Collaborating across teams to establish data governance, observability, and reliability.

Responsibilities

Own and evolve the data platform - the BigQuery warehouse, dbt transformation layers, Airflow / Cloud Composer orchestration and Pub/Sub ingestion that feed every model and metric.
Build and operate the ML platform - training pipelines (Kubeflow on Vertex AI), model serving (FastAPI behind Vertex endpoints), CI/CD, containerization and typed contracts.
Take operational ownership of model-serving infrastructure so reliability isn't carried by the data scientists alone.
Harden and standardize the data models the business depends on - improving schemas, fixing data-quality issues and establishing trustworthy source-of-truth feeds.
Establish data governance and observability - bring data that lives outside the warehouse under proper governance and build operational metrics for products that don't yet have them.
Standardize how data engineering is done across product lines - patterns, tooling and pipelines other teams can adopt.
Partner across data science, backend and product on the producer to consumer contract (models produced by data science, consumed/aggregated downstream, surfaced to clients).

5+ years of hands-on Data Engineering experience designing, building, and operating production data platforms, pipelines, and warehouse solutions in a cloud environment.
Strong experience with ETL/ELT development, data modeling, schema design, orchestration, data quality, lineage, and warehouse optimization.
Expert SQL and strong Python skills, with the ability to build scalable, maintainable, and well-tested data solutions that support both operational and analytical workloads.
Experience working with modern cloud-native data ecosystems, including data warehouses, event-driven architectures, distributed processing, and platform observability.
Demonstrated ownership of production systems, including monitoring, reliability, performance tuning, cost optimization, incident response, and ongoing platform improvements.
Experience supporting machine learning workflows, feature pipelines, model-serving infrastructure, or MLOps environments is an asset.
Ability to partner effectively with Data Science, Product, Engineering, and QA teams to deliver trusted, scalable, and well-governed data solutions.
Bachelor's degree in Computer Science, Data Engineering, Software Engineering, or a related technical field, or equivalent practical experience.
Must be legally authorized to work in Canada.