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Ingénieur(e) de données principal(e) | Senior Data Engineer
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Ingénieur(e) de données principal(e) | Senior Data Engineer
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Full-time
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Valsoft Corporation

Ingénieur(e) de données principal(e) | Senior Data Engineer

Montreal, QC, Canada
Saint-Laurent, QC, Canada

Job type: Full-time
Posting language: English · French
Posted: Sep 18, 2026

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Original job description

English section of this bilingual listing.

ABOUT VALPAY

At Valpay, we're building the next generation of embedded payments. We help SaaS companies turn payments from a utility into a new line of revenue - our PayFac-as-a-Service model delivers the benefits of integrated payments while we absorb the complexity.

More than 3,000 merchants across 12 verticals in North America, Europe and Australia run on our platform. Our Growth Pods operate like small business units: each one owns the software partners it acquires, the merchants it activates, and the revenue it grows.

THE ROLE

As a Senior Data Engineer, you'll design, build and operate the data platform behind Valpay's analytics, reporting, product development and day-to-day operational decisions. Payments data is our core asset - every transaction, settlement and merchant interaction flows through systems you'll help shape.

You'll work alongside Engineering, Product, Finance, Operations and the Growth Pods to make data reliable, accessible and trusted across the company, and increasingly to make it usable by the AI-powered tools and features we're building on top of it.

This is an on-site role at our Montreal office. We build in person: our data, engineering and Growth Pod teams sit together, and the quickest way to untangle a payments data problem is at a whiteboard with the people who own the system.

WHAT YOU'LL DO

Design, build and maintain scalable data pipelines and ELT/ETL processes across both batch and streaming workloads.
Model and optimize our Snowflake warehouse - dimensional models, dbt transformations, and the semantic layer that analytics and product teams build on.
Build the data foundations for payments reporting: transaction lifecycle, settlement and reconciliation, merchant performance and partner revenue.
Own data quality, integrity, lineage and observability. You'll define what "trustworthy" means here and build the tooling that proves it.
Improve the performance, reliability and cost efficiency of the platform as transaction volumes grow.
Prepare and serve data for AI and LLM use cases - clean, well-documented, well-governed datasets that internal AI tools and customer-facing features can depend on.
Partner with stakeholders across the business to turn open-ended questions into well-scoped data solutions.
Work with software engineers to embed data solutions into customer-facing products and internal systems.
Set and document the standards, patterns and practices the data team will grow into.

WHAT YOU'LL BRING

5+ years building and running production data systems as a Data Engineer, Analytics Engineer, or in a closely related role.
Advanced SQL and strong Python (or a comparable language), with production engineering habits - version control, testing, code review, CI/CD.
Hands-on experience with our core stack: Snowflake, dbt and Airflow, or close equivalents you can carry across quickly.
Strong data modeling and warehousing fundamentals - dimensional modeling, incremental patterns, slowly changing dimensions - and a working understanding of distributed data systems.
Experience on a major cloud platform (AWS, Azure or GCP).
A track record of working cross-functionally and explaining technical trade-offs to people who don't share your background.
A degree in Computer Science, Engineering, Mathematics, Statistics or a related quantitative field - or equivalent hands-on experience.

NICE TO HAVE

Experience in payments, fintech, financial services or another transaction-heavy, accuracy-critical environment.
Direct exposure to embedded payments, PayFac models, merchant acquiring or payment processing.
Experience building data infrastructure for AI/LLM applications - retrieval pipelines, vector stores, context or feature layers, evaluation datasets.
Fluency with AI-assisted development tools in your day-to-day engineering work.
Real-time streaming and event-driven architectures (Kafka, Kinesis or similar).
Familiarity with data governance, privacy and security in regulated environments (PCI DSS, SOC 2, Quebec's Law 25, GDPR).
Experience building customer-facing data products or analytics platforms.

WHAT SUCCESS LOOKS LIKE

In your first 90 days, you know our payments data model end to end and have shipped improvements to the pipelines the business depends on most.
By six months, data quality and observability are measurably better - teams trust the numbers and stop rebuilding their own versions of them.
By twelve months, the platform handles materially higher volume without a proportional rise in cost or incidents, and the patterns you've set are how the team builds by default.

WHY VALPAY

Foundational work, not maintenance. You'll make architecture decisions that shape the platform for years, at a company still early enough for those decisions to matter.
Payments at scale, without the bureaucracy. 3,000+ merchants across three continents, and a team small enough that you'll know everyone building it.
Visible impact. You'll see your work in the numbers the business runs on.
Competitive compensation, comprehensive benefits, and room to grow - technically or into leadership.

WORKING AT VALPAY

This is a full-time, on-site position based in our Montreal office.

Valpay is an equal opportunity employer. We welcome applications from all qualified candidates and are committed to an inclusive, barrier-free hiring process. If you need an accommodation at any point in the process, let us know and we'll work with you.

___________________________________________________________________________________________

Read the full bilingual original

ABOUT VALPAY

THE ROLE

WHAT YOU'LL DO

Design, build and maintain scalable data pipelines and ELT/ETL processes across both batch and streaming workloads.

Model and optimize our Snowflake warehouse - dimensional models, dbt transformations, and the semantic layer that analytics and product teams build on.

Build the data foundations for payments reporting: transaction lifecycle, settlement and reconciliation, merchant performance and partner revenue.

Own data quality, integrity, lineage and observability. You'll define what "trustworthy" means here and build the tooling that proves it.

Improve the performance, reliability and cost efficiency of the platform as transaction volumes grow.

Prepare and serve data for AI and LLM use cases - clean, well-documented, well-governed datasets that internal AI tools and customer-facing features can depend on.

Partner with stakeholders across the business to turn open-ended questions into well-scoped data solutions.

Work with software engineers to embed data solutions into customer-facing products and internal systems.

Set and document the standards, patterns and practices the data team will grow into.

WHAT YOU'LL BRING

5+ years building and running production data systems as a Data Engineer, Analytics Engineer, or in a closely related role.

Advanced SQL and strong Python (or a comparable language), with production engineering habits - version control, testing, code review, CI/CD.

Hands-on experience with our core stack: Snowflake, dbt and Airflow, or close equivalents you can carry across quickly.

Strong data modeling and warehousing fundamentals - dimensional modeling, incremental patterns, slowly changing dimensions - and a working understanding of distributed data systems.

Experience on a major cloud platform (AWS, Azure or GCP).

A track record of working cross-functionally and explaining technical trade-offs to people who don't share your background.

A degree in Computer Science, Engineering, Mathematics, Statistics or a related quantitative field - or equivalent hands-on experience.

NICE TO HAVE

Experience in payments, fintech, financial services or another transaction-heavy, accuracy-critical environment.

Direct exposure to embedded payments, PayFac models, merchant acquiring or payment processing.

Experience building data infrastructure for AI/LLM applications - retrieval pipelines, vector stores, context or feature layers, evaluation datasets.

Fluency with AI-assisted development tools in your day-to-day engineering work.

Real-time streaming and event-driven architectures (Kafka, Kinesis or similar).

Familiarity with data governance, privacy and security in regulated environments (PCI DSS, SOC 2, Quebec's Law 25, GDPR).

Experience building customer-facing data products or analytics platforms.

WHAT SUCCESS LOOKS LIKE

In your first 90 days, you know our payments data model end to end and have shipped improvements to the pipelines the business depends on most.

By six months, data quality and observability are measurably better - teams trust the numbers and stop rebuilding their own versions of them.

By twelve months, the platform handles materially higher volume without a proportional rise in cost or incidents, and the patterns you've set are how the team builds by default.

WHY VALPAY

Foundational work, not maintenance. You'll make architecture decisions that shape the platform for years, at a company still early enough for those decisions to matter.

Real ownership. Our Growth Pods run like small businesses. Data isn't a service desk here - it's how decisions get made.

Payments at scale, without the bureaucracy. 3,000+ merchants across three continents, and a team small enough that you'll know everyone building it.

Visible impact. You'll see your work in the numbers the business runs on.

Competitive compensation, comprehensive benefits, and room to grow - technically or into leadership.

WORKING AT VALPAY

This is a full-time, on-site position based in our Montreal office.

___________________________________________________________________________________________

À PROPOS DE VALPAY

Chez Valpay, nous bâtissons la prochaine génération de paiements intégrés. Nous aidons les entreprises SaaS à transformer les paiements, d'un simple service utilitaire en une nouvelle source de revenus : notre modèle PayFac as a Service leur en procure tous les avantages pendant que nous absorbons la complexité.

Plus de 3 000 marchands répartis dans 12 secteurs verticaux en Amérique du Nord, en Europe et en Australie utilisent notre plateforme. Nos Growth Pods fonctionnent comme de petites unités d'affaires : chacune est responsable des partenaires logiciels qu'elle recrute, des marchands qu'elle active et de la croissance des revenus qu'elle génère.

LE POSTE

À titre d'ingénieur(e) de données principal(e), vous concevrez, bâtirez et exploiterez la plateforme de données qui soutient l'analytique, la production de rapports, le développement de produits et les décisions opérationnelles quotidiennes de Valpay. Les données de paiement sont notre actif central : chaque transaction, chaque règlement et chaque interaction marchande transite par des systèmes que vous contribuerez à façonner.

Vous collaborerez avec les équipes d'ingénierie, de produit, des finances, des opérations et les Growth Pods afin de rendre les données fiables, accessibles et dignes de confiance dans toute l'entreprise - et, de plus en plus, exploitables par les outils et fonctionnalités alimentés par l'IA que nous développons par-dessus.

Ce poste s'exerce en présentiel à notre bureau de Montréal. Nous bâtissons ensemble, sur place : nos équipes de données, d'ingénierie et nos Growth Pods travaillent côte à côte, et la façon la plus rapide de dénouer un problème de données de paiement reste le tableau blanc, avec les personnes qui connaissent le système.

VOS RESPONSABILITÉS

Concevoir, bâtir et maintenir des pipelines de données évolutifs et des processus ELT/ETL, en traitement par lots comme en continu.

Modéliser et optimiser notre entrepôt Snowflake : modèles dimensionnels, transformations dbt et couche sémantique sur laquelle s'appuient les équipes d'analytique et de produit.

Bâtir les fondations de données de la production de rapports sur les paiements : cycle de vie des transactions, règlement et rapprochement, performance des marchands et revenus des partenaires.

Assumer la responsabilité de la qualité, de l'intégrité, de la traçabilité et de l'observabilité des données. Vous définirez ce que « fiable » signifie ici et bâtirez les outils qui le démontrent.

Améliorer la performance, la fiabilité et l'efficience des coûts de la plateforme à mesure que les volumes de transactions augmentent.

Préparer et livrer les données destinées aux cas d'usage d'IA et de grands modèles de langage (GML) : jeux de données propres, bien documentés et bien encadrés, sur lesquels nos outils internes d'IA et nos fonctionnalités clients pourront compter.

Travailler avec les parties prenantes de l'entreprise pour transformer des questions ouvertes en solutions de données bien cadrées.

Collaborer avec les développeurs pour intégrer les solutions de données aux produits destinés aux clients et aux systèmes internes.

Établir et documenter les normes, les pratiques et les modèles de référence qui guideront la croissance de l'équipe de données.

VOTRE PROFIL

5 ans ou plus d'expérience à bâtir et à exploiter des systèmes de données en production, à titre d'ingénieur(e) de données, d'ingénieur(e) analytique ou dans un rôle étroitement lié.

Maîtrise avancée du SQL et solide maîtrise de Python (ou d'un langage comparable), avec de bonnes pratiques d'ingénierie en production : gestion de versions, tests, revue de code, CI/CD.

Expérience pratique de notre pile technologique : Snowflake, dbt et Airflow, ou des équivalents proches que vous pouvez transposer rapidement.

Solides bases en modélisation et en entreposage de données - modélisation dimensionnelle, traitements incrémentiels, dimensions à évolution lente - et bonne compréhension des systèmes de données distribués.

Expérience d'une grande plateforme infonuagique (AWS, Azure ou GCP).

Un parcours démontrant votre capacité à travailler de façon transversale et à expliquer des compromis techniques à des personnes qui ne partagent pas votre expertise.

Un diplôme en informatique, en génie, en mathématiques, en statistique ou dans une discipline quantitative connexe - ou une expérience pratique équivalente.

ATOUTS SUPPLÉMENTAIRES

Expérience dans les paiements, les technologies financières, les services financiers ou un autre environnement à fort volume transactionnel où l'exactitude est critique.

Exposition directe aux paiements intégrés, aux modèles PayFac, à l'acquisition de marchands ou au traitement des paiements.

Expérience de la construction d'infrastructures de données pour des applications d'IA ou de GML : pipelines de recherche documentaire, bases vectorielles, couches de contexte ou de caractéristiques, jeux de données d'évaluation.

Aisance avec les outils de développement assistés par l'IA dans votre travail quotidien.

Architectures de diffusion en continu en temps réel et architectures événementielles (Kafka, Kinesis ou équivalents).

Connaissance de la gouvernance, de la confidentialité et de la sécurité des données en environnement réglementé (PCI DSS, SOC 2, Loi 25, RGPD).

Expérience de la création de produits de données ou de plateformes analytiques destinés aux clients.

CE À QUOI RESSEMBLE LA RÉUSSITE

Après 90 jours, vous maîtrisez notre modèle de données de paiement de bout en bout et avez livré des améliorations aux pipelines les plus critiques pour l'entreprise.

Après six mois, la qualité et l'observabilité des données se sont améliorées de façon mesurable : les équipes se fient aux chiffres et cessent d'en reconstruire leurs propres versions.

Après douze mois, la plateforme absorbe des volumes nettement plus élevés sans hausse proportionnelle des coûts ni des incidents, et les modèles que vous avez établis sont devenus la façon de faire par défaut de l'équipe.

POURQUOI VALPAY

Du travail fondateur, pas de l'entretien. Vous prendrez des décisions d'architecture qui façonneront la plateforme pour des années, dans une entreprise encore assez jeune pour que ces décisions comptent.

De la véritable autonomie. Nos Growth Pods fonctionnent comme de petites entreprises. Ici, les données ne sont pas un centre de service : c'est ainsi que les décisions se prennent.

Des paiements à grande échelle, sans la bureaucratie. Plus de 3 000 marchands sur trois continents, et une équipe assez petite pour que vous connaissiez toutes les personnes qui la bâtissent.

Un impact visible. Vous verrez votre travail dans les chiffres qui font tourner l'entreprise.

Une rémunération concurrentielle, une gamme complète d'avantages sociaux et de réelles possibilités d'évolution, sur le plan technique comme vers la gestion.

TRAVAILLER CHEZ VALPAY

Il s'agit d'un poste à temps plein, en présentiel, à notre bureau de Montréal.

Valpay souscrit au principe de l'égalité d'accès à l'emploi. Nous accueillons les candidatures de toutes les personnes qualifiées et nous nous engageons à offrir un processus d'embauche inclusif et sans obstacle. Si vous avez besoin d'une mesure d'adaptation à une étape ou l'autre du processus, faites-le-nous savoir et nous trouverons une solution avec vous.

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More about this job

Responsibilities

Design, build, and maintain scalable data pipelines and ELT/ETL processes while modeling and optimizing the Snowflake warehouse. Collaborate with cross-functional teams to ensure data reliability, quality, and accessibility for analytics, product development, and AI-powered features.

Requirements

Requires 5+ years of experience in data engineering with advanced proficiency in SQL and Python. Candidates must have hands-on experience with Snowflake, dbt, and Airflow, along with strong data modeling fundamentals.

Skills

Data Engineering
SQL
Python
Snowflake
dbt
Airflow
Data Modeling
Data Warehousing
ETL
ELT
Data Pipelines
Cloud Platforms
Distributed Systems
Data Quality
Observability
Payments

Benefits

Competitive compensation
Comprehensive benefits

Visa sponsorship

Not detected in the job text

Keywords

Data Engineering
Snowflake
dbt
Airflow
SQL
Python
ETL
ELT
Data Modeling
Data Warehousing
Payments
Fintech
Cloud Computing
AWS
Azure
GCP
AI
LLM
Data Governance
Streaming
Kafka
Kinesis
CI/CD
Dimensional Modeling
Observability
Data Lineage
SaaS
PayFac
Merchant Acquiring
Data Platform

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