Flinks

Flinks

Flinks is a top financial data company empowering businesses with bank account connectivity, data insights, and open banking success.

Capital Markets
51-250
Founded 2016
$14M raised

Description

  • Own and evolve the data platform, including BigQuery, dbt, Airflow/Cloud Composer, and Pub/Sub ingestion.
  • Build and operate the ML platform, including training pipelines, model serving infrastructure, CI/CD, containerization, and typed contracts.
  • Take operational ownership of model-serving infrastructure so production reliability does not rest solely with data scientists.
  • Harden and standardize core data models by improving schemas, fixing data-quality issues, and establishing trusted source-of-truth feeds.
  • Establish data governance and observability for data outside the warehouse and create operational metrics for products that lack them.
  • Standardize data engineering practices across product lines through reusable patterns, tooling, and pipelines.
  • Partner with data science, backend, product, and QA teams on producer-to-consumer data contracts and downstream consumption.
  • Build scalable data pipelines and maintain reliable datasets, feature pipelines, and data services for real-time and batch workloads.
  • Improve data quality, observability, performance, and cost efficiency across the platform and customer-facing products.
  • Contribute to the evolution of the data platform architecture as Flinks scales its products and machine learning capabilities.

Requirements

  • 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.
  • Experience with BigQuery, dbt, Airflow, or equivalent modern data tooling is highly desirable.
  • Experience 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 improvements.
  • Experience supporting machine learning workflows, feature pipelines, model-serving infrastructure, or MLOps environments is an asset.
  • Ability to collaborate effectively with Data Science, Product, Engineering, and QA teams.
  • 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.

Benefits

  • Compensation range of $120,000 to $160,000 CAD annually for experienced qualified hires in Canada at the IC4 level.
  • Health and dental coverage starting on day 1.
  • Flexible paid time off (FTO).
  • Remote work environment with frequent in-person gatherings and activities.
  • Career development, learning opportunities, and growth support.
  • Accommodation support for persons with disabilities during the recruitment or employment process.
  • Use of AI in recruitment to assist with screening, assessment, or selection.

Interested in this position?

Apply directly on the company website

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