Senior Data Engineer

Flinks•Montreal, QC

5d•CA$120,000 - CA$160,000•Remote

About The Position

Flinks is the embedded finance platform that brings together connectivity, intelligence, and payments — giving businesses the infrastructure they need to build and deliver seamless financial experiences at scale. As a leader in Open Finance in Canada, we’ve grown since 2016 into one of North America’s most trusted platforms for financial data access, enrichment, and money movement. We work with innovators across many industries, including lending, fintech, banking, insurance, and wealth management. Today, our platform connects to 15,000+ financial institutions across North America and powers over 1M monthly connections. We also give our customers unprecedented visibility into 4,500+ real-time financial insights to support smarter decisioning. Companies rely on Flinks to streamline onboarding, verify income, assess credit risk, and power faster payment experiences. We’re on a mission to drive financial innovation and help businesses build financial experiences that feel effortless, connected, and customer-first. That’s where you come in. 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.

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. Experience with BigQuery, dbt, Airflow, or equivalent modern data tooling is highly desirable.
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.
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.

Nice To Haves

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.

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).