Senior Data Engineer

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Flinks

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2016

💳 Fintech

🏦 Banking

💸 Finance

Fintech • Banking • Finance

Flinks is a company that provides financial data infrastructure to power innovation in the financial sector. It enables users to safely connect their bank accounts and transform raw transaction data into actionable insights, supporting open banking programs. Its solutions optimize loan underwriting, enhance digital banking, facilitate payments, and enable wealth management. By leveraging its extensive network of third-party applications, Flinks helps financial institutions innovate and build the financial systems of the future, serving the needs of fintech, wealth management, and traditional banking industries.

📋 Description

• 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).

🎯 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, 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.

🏖️ 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