The Role
We're hiring our Senior Data Engineer (Data / ML Platform) to stand up data engineering as a discipline at Flinks. You'll own the data and ML platform that turns models into reliable production services, harden the data models the business runs on and close the seam between our data scientists and the product teams. This is a high‑ownership, greenfield‑leaning role: much of this foundation is yours to build and own, not inherit.
If you like being the person who makes data and ML production‑grade - pipelines, serving, governance, reliability - and you want broad impact across a company's data, this is built for you.
What You’ll Do
- 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).
What You’ll Work On
You’ll help build and evolve the data platform that powers Flinks' financial intelligence products, supporting everything from transaction enrichment and categorization to risk and payments decisioning.
Key areas of focus include:
- Building scalable data pipelines that process and transform large volumes of financial data.
- Designing and maintaining reliable datasets, data models, and feature pipelines used by machine learning and product teams.
- Improving data quality, observability, and operational metrics across our platform and customer‑facing products.
- Developing cost‑efficient, high‑performance data services and infrastructure that support real‑time and batch workloads.
- Partnering closely with Data Science, Product, and Engineering teams to enable new capabilities and accelerate product delivery.
- Contributing to the evolution of our data platform architecture as we continue to scale our products, customers, and machine learning capabilities.
Our stack
- Python, SQL, Bash
- Google Cloud Platform (GCP)
- BigQuery and dbt
- Airflow (Cloud Composer), Pub/Sub, and Cloud Functions
- Kubeflow, Vertex AI, MLflow, and FastAPI
- Docker, Terraform, and Protocol Buffers
- Azure DevOps
- Grafana and GCP Logging
You don’t need experience with every tool listed above - strong Data Engineering fundamentals and experience building production data platforms matter more than direct experience with our exact stack. SQL is the exception: it's a non‑negotiable (see Key Requirements).
Key Requirements
- Experience: 5+ years of hands‑on Data Engineering experience designing, building, and operating production data platforms, pipelines, and warehouse solutions in a cloud environment.
- Data Engineering Expertise: Strong experience with ETL/ELT development, data modeling, schema design, orchestration, data quality, lineage, and warehouse optimization. Experience with BigQuery, dbt, Airflow, or equivalent modern data tooling is highly desirable.
- Technical Foundation: 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.
- Cloud Data Platforms: Experience working with modern cloud‑native data ecosystems, including data warehouses, event‑driven architectures, distributed processing, and platform observability.
- Operational Excellence: Demonstrated ownership of production systems, including monitoring, reliability, performance tuning, cost optimization, incident response, and ongoing platform improvements.
- Machine Learning Platform Exposure: Experience supporting machine learning workflows, feature pipelines, model‑serving infrastructure, or MLOps environments is an asset, but a strong Data Engineering foundation is the primary requirement.
- Collaboration: Ability to partner effectively with Data Science, Product, Engineering, and QA teams to deliver trusted, scalable, and well‑governed data solutions.
- Education: Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, or a related technical field, or equivalent practical experience.
- Work Authorization: Must be legally authorized to work in Canada.
Compensation Range
For experienced and qualified hires located in Canada, of senior (IC4) level, the compensation range is between $120,000 to $160,000 CAD annually.
Benefits
- Health & Dental coverage as of Day 1
- Flexible Paid Time Off (FTO)
- Remote work environment with frequent in‑person gatherings and activities.
- Career development, learning opportunities and growth
- And more
Accessibility
We are committed to providing accommodations for persons with disabilities. If you require accommodation, we will work with you to meet your needs.
Intelligence Artificielle et Recrutement
Flinks uses artificial intelligence (AI) during the recruitment process to assist in screening, assessing, or selecting applicants.