Senior Data Engineer

Hire DigITalent Inc.; Toronto; On-site; CAD 120,000 - 170,000; Full time

Job summary

Hire DigITalent Inc. is seeking a Senior Data Engineer to lead Snowflake-based data pipelines and the core data platform. You will own end-to-end data engineering initiatives, drive performance, and partner with analysts, data scientists, and product teams to deliver reliable data assets.

The role emphasizes governance, scalable architectures, and best-practice engineering across ETL/ELT workflows while mentoring junior engineers.

Qualifications

• 4–5+ years hands-on Snowflake production experience.
• 5–7 years data engineering experience, senior/lead role.
• Bachelor’s degree in CS/SE/IS or related field.

Responsibilities

• Design, build, and deploy Snowflake-based ETL/ELT pipelines and orchestration workflows.
• Define data modeling, lineage, governance practices for data assets.
• Set standards for code reviews, version control, automated testing, CI/CD.
• Tune Snowflake performance, storage tiers, and cost optimization.
• Collaborate with analysts, data scientists, product leaders, and partners.
• Monitor pipeline reliability and respond to incidents.
• Automate pipelines and explore new technologies for platform growth.
• Mentor team and promote agile practices to maintain momentum.

Skills

Snowflake

Python

SQL

Data modeling

Airflow

CI/CD

Data governance

Mentorship

Education

Bachelor's degree in Computer Science / Software Engineering / Information Systems

Tools

Azure Data Factory

Git workflows

Cloud (Azure / GCP)

Job description

The Senior Data Engineer will play a key role within a high-performing engineering team, driving the design, delivery, and ongoing operation of scalable data pipelines that empower organizational decision-making. This role is responsible for taking projects from concept to completion on schedule while ensuring the stability, performance, and overall health of the core data platform.

MUST-HAVE REQUIREMENT

• 4–5+ years of direct, hands-on experience building, optimizing, and managing Snowflake data architectures in production environments.

KEY PERFORMANCE INDICATORS

• On-time and on-scope execution of core data engineering projects.
• High pipeline uptime, SLA compliance, and minimal Mean Time to Detect/Resolve (MTTD/MTTR) incidents.
• Strong data health metrics, including high schema validation success rates, accuracy, and completeness.
• Continuous elevation of engineering practices, team engagement, and technical capabilities.

KEY RESPONSIBILITIES

Core Engineering & Architecture

• Snowflake & Pipeline Development: Responsible for the design, construction, and deployment of scalable ETL/ELT pipelines and orchestration workflows centered around Snowflake to supply clean data across analytics and operational systems.
• Data Standards & Governance: Define and maintain data modeling, lineage, and governance practices to keep data assets consistent, secure, and reliable.
• Engineering Best Practices: Set strict standards for code reviews, version control, automated testing, and CI/CD releases to guarantee all deliverables are maintainable and well-documented.
• Platform Optimization: Continuously tune and manage cloud data infrastructure—focusing on Snowflake performance, query optimization, storage tiers, and cost efficiency.
• Stakeholder Alignment: Work alongside data analysts, data scientists, product leaders, and business partners to convert operational requirements into clear technical specifications, accurate project timelines, and transparent status updates.
• Monitoring & Incident Response: Maintain continuous oversight of pipeline reliability, acting quickly to troubleshoot and resolve infrastructure issues as they arise.
• Process Automation & Innovation: Eliminate technical debt and manual steps by automating pipelines and testing emerging technologies to expand platform capabilities.
• Team Mentorship: Support team growth through direct technical guidance and agile practices (sprint planning, backlog refinement, retrospectives) to maintain high delivery momentum.
• Stakeholder-Centric Mindset: Serve as an advocate for internal and external end-users by incorporating their needs into every technical decision, challenging legacy processes, and pursuing innovative technical solutions.

QUALIFICATIONS & SKILLS

Required Experience

• Crucial: 4–5+ years of extensive, hands-on experience with Snowflake (including platform setup, complex data modeling, query optimization, and cost governance).
• 5–7 years of overall data engineering experience designing and maintaining production-grade data platforms, with at least 2 years in a senior or lead role.
• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related field (or equivalent practical experience).

Technical & Professional Competencies

• Deep proficiency in SQL and Python for complex data transformation and workflow automation.
• Proven background using cloud environments (Azure or GCP preferred) alongside modern data orchestration tools (e.g., Airflow, Azure Data Factory, or similar).
• Strong grasp of data modeling concepts (dimensional modeling, star schema, data vault) and data quality/observability frameworks.
• Practical knowledge of Git workflows and CI/CD deployment pipelines within data platforms.
• Clear communication skills, with a proven ability to translate complex technical concepts for business stakeholders.
• Relevant cloud certifications (e.g., Snowflake SnowPro Core/Advanced, Azure Data Engineer Associate, or Google Professional Data Engineer) are considered a strong asset.