Senior Data Engineer

Senior Data Engineer

Project: Canada Data

Technologies: Github/Bitbucket/Gitlab SQL Server Terraform Amazon Web Services (AWS) Python Snowflake Azure

Seniority: Senior

*LOCATION: CANADA*

People’s Group is a leading Canadian financial services provider headquartered in Toronto, specializing in customized banking, lending, and payment solutions for a broad spectrum of clients across Canada. The organization is recognized for its reliability, innovation, and client-first approach, with a strong focus on technological advancement and secure, flexible solutions.

*Position Overview*

We are looking for a Senior Data Engineer to design, build, and optimize our next-generation data platform. In this role, you will be the architect and builder of scalable data pipelines, ensuring our data ecosystem is reliable, efficient, and built for growth. You will collaborate closely with Tech Leads, analysts, and product teams to turn raw data into a strategic asset.

If you thrive in a collaborative environment, love optimizing complex data architectures, and believe that data pipelines should be treated with the same engineering rigor as production software, lets discuss.

*Key Responsibilities*

*Data Architecture & Modeling:* Design and implement scalable, robust data models within our cloud data warehouse ( *Snowflake* ) to support diverse analytics and reporting needs.

*Pipeline Development (ELT/ETL):* Build and maintain modular, tested, and well-documented data transformation pipelines using *dbt (data build tool).*

*Orchestration:* Design complex workflow orchestrations and dependency graphs using *Dagster* or *Apache Airflow* , ensuring high availability and fault tolerance.

*Code Quality & Automation:* Write clean, efficient, and reusable *Python* code for data functions and pipeline development and monitoring of data assets.

*Cloud Infrastructure:* Leverage cloud services (AWS/Google Cloud Platform/Azure) to manage data lake storage, compute resources, and secure data sharing.

*Coding Best Practices:* Perform code reviews, champion software engineering best practices (version control, testing, documentation) within the data team.

*Tech Stack We Use*

You don''t need to be an expert in all of these, but this is what our environment looks like:

*Data Warehouse:* Snowflake

*Transformations:* dbt Core / dbt Cloud

*Orchestration:* Dagster or Apache Airflow

*Language:* Python, Advanced SQL

*Cloud Platform:* AWS and Azure

*Infrastructure as Code:* Terraform (preferred but not required)

*CI/CD:* GitHub Actions

*Qualifications & Skills*

Minimum Qualifications

*Experience:* 5+ years of experience in data engineering, software engineering, or a related quantitative field.

*Snowflake Expertise:* Deep understanding of Snowflake architecture (virtual warehouses, clustering, micro-partitions, snowpipe, and cost optimization).

*Modern Data Stack:* Proven track record of putting dbt into production at scale.

*Orchestration Mastery:* Strong experience managing production workflows using Dagster or Airflow (writing custom operators, sensors, or software-defined assets).

*Programming:* Advanced proficiency in Python (pandas, pytest, poetry/pipenv) and writing highly optimized, complex SQL.

*Cloud Environment:* Hands-on experience with cloud infrastructure, including managed services, IAM policies, and cloud storage (e.g., AWS S3, Google Cloud Storage).

*Scope of Work*

The Data Engineer will be expected to:

Contribute to end-to-end project ownership, taking a defined component from requirements through delivery, with full accountability for quality and documentation

Work on metadata storage strategy and log management, defining the right architecture and approach for internal data infrastructure

Support multiple concurrent projects as the team scales, without requiring day-to-day supervision from the team lead

Communicate directly with business stakeholders to gather and manage requirements, including raising scope changes proactively

Follow and contribute to established development practices and pull request standards (dual approvals, documentation, test case evidence)

Raise blockers early and bring solutions rather than waiting for direction

*Working Style & Expectations*

Must operate with senior-level autonomy — the lead cannot be in every meeting or review every decision

Expected to voice technical opinions in group settings and lead peers toward correct design decisions, not defer to junior members

Must collaborate seamlessly across team members on shared projects

The lead exposes all team members to stakeholders — the expectation is that the engineer can represent the team professionally.

Nota para el entrevistador:

*Pre-screening question:*

*"Walk us through a dbt-based data pipeline you built and took to production. What was the business problem, how did you structure your dbt models and orchestration (Dagster or Airflow), and what did you put in place to ensure it stayed reliable once it was live?"*