{"id":871405,"url":"https://alion.io/job/flinks-senior-data-engineer","title":"Senior Data Engineer / Ingénieur(e) de données senior","company":{"id":58125,"name":"Flinks","domain":"flinks.com","url":"https://alion.io/company/flinks","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Montreal, Canada"],"countries":["CA"],"hiring_countries":["CA"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":72000,"max_usd":139000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":17},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"dbt","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FastAPI","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Grafana","optional":false},{"name":"Kubeflow","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Vertex AI","optional":false},{"name":"Azure","optional":true}],"status":"live","first_seen_at":"2026-09-04T00:00:00Z","employer_posted_date":"2026-09-04","last_verified_at":"2026-11-06T02:14:30Z","board_verified":true,"closed_at":null,"days_open":63,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":63},"description":"About Flinks\nFlinks 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.\nAs 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.\nToday, 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.\nWe’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.\nThe Role\nWe'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.\nIf 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.\nWhat You'll Do\nOwn 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.\n 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.\nHarden and standardize the data models the business depends on - improving schemas, fixing data-quality issues and establishing trustworthy source-of-truth feeds.\nEstablish 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.\nStandardize how data engineering is done across product lines - patterns, tooling and pipelines other teams can adopt.\n Partner across data science, backend and product on the producer to consumer contract (models produced by data science, consumed/aggregated downstream, surfaced to clients).\nWhat You'll Work On\nYou'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.\nKey areas of focus include:\nBuilding scalable data pipelines that process and transform large volumes of financial data. \n Designing and maintaining reliable datasets, data models, and feature pipelines used by machine learning and product teams. \n Improving data quality, observability, and operational metrics across our platform and customer-facing products. \n Developing cost-efficient, high-performance data services and infrastructure that support real-time and batch workloads. \n Partnering closely with Data Science, Product, and Engineering teams to enable new capabilities and accelerate product delivery. \n Contributing to the evolution of our data platform architecture as we continue to scale our products, customers, and machine learning capabilities.\nOur stack\nPython, SQL, Bash \nGoogle Cloud Platform (GCP) \nBigQuery and dbt \nAirflow (Cloud Composer), Pub/Sub, and Cloud Functions \nKubeflow, Vertex AI, MLflow, and FastAPI \nDocker, Terraform, and Protocol Buffers \nAzure DevOps \nGrafana and GCP Logging \nYou 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).\nWhy This Role\n Greenfield ownership - help build and evolve the data platform that powers Flinks' next generation of data and machine learning products. \nHigh leverage impact - your work enables Data Science, Product, Engineering, and Risk teams to move faster with reliable, trusted data. \nReal-world scale and complexity - work with large volumes of financial data powering products used by banks, fintechs, and financial institutions across North America. \nModern cloud-native environment - build on a modern GCP stack using contemporary data, platform, and machine learning tooling.\nKey Requirements\nExperience: 5+ years of hands-on Data Engineering experience designing, building, and operating production data platforms, pipelines, and warehouse solutions in a cloud environment.\nData 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.\nTechnical 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.\nCloud Data Platforms: Experience working with modern cloud-native data ecosystems, including data warehouses, event-driven architectures, distributed processing, and platform observability.\nOperational Excellence: Demonstrated ownership of production systems, including monitoring, reliability, performance tuning, cost optimization, incident response, and ongoing platform improvements.\nMachine 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.\nCollaboration: Ability to partner effectively with Data Science, Product, Engineering, and QA teams to deliver trusted, scalable, and well-governed data solutions.\nEducation: Bachelor's degree in Computer Science, Data Engineering, Software Engineering, or a related technical field, or equivalent practical experience.\nWork Authorization: Must be legally authorized to work in Canada.\nBenefits\nAs part of the total rewards package, Flinks offers:\nHealth & Dental coverage as of Day 1\nFlexible Paid Time Off (FTO)\nRemote work environment with frequent in-person gatherings and activities.\nCareer development, learning opportunities and growth\nAnd more\nWe are committed to providing accommodations for persons with disabilities. If you require accommodation, we will work with you to meet your needs.\nFlinks uses artificial intelligence (AI) during the recruitment process to assist in screening, assessing, or selecting applicants.\n---------------------------------------------------------------------------------------\nÀ propos de Flinks\nFlinks est une plateforme de finance intégrée qui réunit la connectivité, l’intelligence financière et les paiements, offrant aux entreprises l’infrastructure nécessaire pour concevoir et déployer des expériences financières fluides à grande échelle.\nChef de file de la finance ouverte (Open Finance) au Canada, nous avons connu une croissance soutenue depuis 2016 pour devenir l’une des plateformes les plus fiables en Amérique du Nord en matière d’accès aux données financières, d’enrichissement des données et de mouvement de fonds. Nous collaborons avec des entreprises innovantes dans de nombreux secteurs, notamment le crédit, les technologies financières (fintech), les services bancaires, l’assurance et la gestion de patrimoine.\nAujourd’hui, notre plateforme est connectée à plus de 15 000 institutions financières à travers l’Amérique du Nord et soutient plus d’un million de connexions mensuelles. Nous offrons également à nos clients un accès à plus de 4 500 indicateurs financiers en temps réel afin de favoriser une prise de décision plus éclairée. Les entreprises s’appuient sur Flinks pour simplifier l’intégration de leurs utilisateurs, vérifier les revenus, évaluer le risque de crédit et offrir des expériences de paiement plus rapides.\nNotre mission est d’accélérer l’innovation financière et d’aider les entreprises à créer des expériences financières simples, connectées et centrées sur leurs clients. C’est là que vous entrez en jeu.\nLe poste\nNous recrutons notre Ingénieur(e) principal(e) en données (Plateforme Données / IA) afin d’établir l’ingénierie des données comme une discipline à part entière chez Flinks. Vous serez responsable de la plateforme de données et d’apprentissage automatique qui transforme les modèles en services de production fiables, renforcerez les modèles de données sur lesquels l’entreprise s’appuie et assurerez la connexion entre nos scientifiques des données et les équipes produit.\nIl s’agit d’un rôle à forte autonomie, orienté vers la création de nouvelles fondations : une grande partie de cette infrastructure sera à concevoir, bâtir et faire évoluer par vous, plutôt qu’à hériter d’un environnement existant.\nSi vous aimez être la personne qui rend les données et l’IA prêtes pour la production - pipelines, mise en service des modèles, gouvernance, fiabilité - et que vous souhaitez avoir un impact important sur l’ensemble des capacités de données d’une entreprise, ce rôle est fait pour vous.\nCe que vous ferez\n Assurer la propriété et l’évolution de la plateforme de données - l’entrepôt BigQuery, les couches de transformation dbt, l’orchestration Airflow / Cloud Composer et les mécanismes d’ingestion Pub/Sub qui alimentent chaque modèle et indicateur. \n Concevoir, exploiter et faire évoluer la plateforme d’apprentissage automatique - pipelines d’entraînement (Kubeflow sur Vertex AI), mise en service des modèles (FastAPI derrière les points de terminaison Vertex), CI/CD, conteneurisation et contrats typés. Assumer la responsabilité opérationnelle de l’infrastructure de mise en production des modèles afin que leur fiabilité ne repose pas uniquement sur les scientifiques des données. \n Renforcer et standardiser les modèles de données essentiels aux opérations de l’entreprise - amélioration des schémas, correction des problèmes de qualité des données et mise en place de sources de vérité fiables. \n Mettre en place des pratiques de gouvernance et d’observabilité des données - intégrer sous une gouvernance appropriée les données qui se trouvent à l’extérieur de l’entrepôt et créer des métriques opérationnelles pour les produits qui n’en disposent pas encore. \n Standardiser les pratiques d’ingénierie des données à travers les différentes lignes de produits - modèles, outils et pipelines pouvant être réutilisés par les autres équipes. \n Collaborer étroitement avec les équipes de science des données, de développement backend et de produit afin de définir et maintenir le contrat entre producteurs et consommateurs de données (modèles produits par la science des données, consommés ou agrégés en aval et exposés aux clients).\nCe sur quoi vous travaillerez\nVous contribuerez à bâtir et à faire évoluer la plateforme de données qui alimente les produits d’intelligence financière de Flinks, soutenant notamment l’enrichissement et la catégorisation des transactions, ainsi que les solutions de gestion du risque et de prise de décision liées aux paiements.\nLes principaux domaines d’intervention incluent :\n Concevoir des pipelines de données évolutifs capables de traiter et de transformer de grands volumes de données financières. \n Concevoir et maintenir des ensembles de données, modèles de données et pipelines de caractéristiques fiables utilisés par les 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