Senior Data Engineer

Cybrid•Toronto, ON

9d•CA$145,000 - CA$185,000•Hybrid

About The Position

We're expanding rapidly and looking for a Senior Data Engineer to define and build the data foundation that powers Cybrid's next chapter. This isn't a maintenance role, it's an opportunity to architect a financial data platform, set the standards for governance and quality, and make AI a first-class collaborator in how we make decisions. This is a chance to build cutting-edge infrastructure, solve complex technical challenges, and shape the future of global payments using the evolving integration of data with AI. As Cybrid's dedicated data engineer, you'll own the data platform end-to-end: pipelines, warehouse, governance, and the AI-augmented workflows it empowers. You'll work directly with Sales, Finance, Compliance, Risk, and Engineering to turn clean, trusted data into a source for our AI agents to make informed decisions, accurate financial modeling, robust risk assessments, and reliable accounting.

Requirements

7+ years of hands-on data engineering experience.
Prior experience with financial services data, crypto, or blockchain integrations.
Expertise in SQL, PostgreSQL, and modern data pipeline frameworks (Airflow, Temporal, Prefect, dbt, etc.).
Proven experience designing and scaling data warehouses and real-time streaming architectures for change data capture (CDC).
Strong background in cloud-based data platforms (AWS, GCP, or Azure).
Knowledge of data modelling best practices in finance.
Knowledge of data quality, governance, and compliance frameworks for sensitive financial data.
Comfort using AI as a daily collaborator in your own work (code generation, query authoring, schema design, documentation) and an interest in scaling that practice across a team.

Nice To Haves

Prior experience in fintech or regulated industries.
Familiarity with modern data stack tools (dbt, Fivetran, Sigma, Looker, Tableau).
Familiarity with dlt for designing managed batch data pipelines.
Experience with Kafka for real-time data pipelines and change data capture upserts.
Exposure to machine learning pipelines, MLOps, or AI-assisted data workflows.
Practical experience integrating AI/LLM workflows into data platforms — RAG over warehouses, semantic data catalogs, agent-driven analytics, or AI-assisted data quality.
Strong knowledge of data security, privacy, and regulatory compliance (e.g., GDPR, PII).
Experience building AI agents that operate against financial systems (reconciliation, classification, anomaly detection) with a human-in-the-loop pattern.

Responsibilities

Define and implement Cybrid’s data engineering strategy, setting up scalable architecture, governance, and technical standards.
Build and maintain robust ETL/ELT pipelines integrating data from microservices and external systems.
Develop a financial data platform and warehouse to unify transaction data for analytics and reporting.
Implement real-time, event-driven data processing pipelines to power compliance, operations, and agentic business intelligence.
Ensure regulatory compliance with strong data quality, governance, lineage, and security practices.
Partner with Sales, Finance, Risk, and Compliance to design AI-augmented workflows utilizing the data platform.
Build the infrastructure that makes AI safe and useful on financial data: retrieval layers, vector stores, semantic data catalogs, and human-in-the-loop review patterns.
Own and evolve Cybrid’s BI and self-serve analytics layer: build the semantic models, dashboards, and access patterns that let all departments pull trusted data without going through engineering.