Data Engineer - SQL, Python, Airflow

Typical Day in Role:

• Support operational excellence through monitoring, incident management, and continuous process improvement 
• Own user requests and data inquiries while actively experimenting, learning, and driving continuous improvement in day-to-day operations .
• Proactively identify gaps and implement scalable improvements, including new analytical patterns and automation to optimize workflows
• Support analysts and data scientists with reliable, timely, and accessible data, while using AI/agent-based solutions to automate and optimize time-consuming processes
• Partner with business teams to understand data needs and deliver actionable insights to management and stakeholders
• Build and maintain scalable data models and ETL/ELT pipelines
• Maintain data quality through automated testing, monitoring, and proactive issue resolution
• Apply data governance, security, and documentation standards across pipelines and architecture

Candidate Requirements/Must Have Skills:

• 5-7 years of experience in SQL (advanced to expert level)
• 3-4 years of experience with workflow orchestration tools such as Apache Airflow
• 3-4 years of experience working with Python for pipeline development.
• 3-4 years of experience with cloud platforms (GCP, AWS, or Azure).
• Experience with modern data stack tools (including dbt)

Nice-To-Have Skills:

• Solid understanding of data warehousing concepts and dimensional modeling
• Knowledge of the banking/finance domain and data governance practices is an asset

Soft skills:
Strong analytical and decision-making abilities.
Excellent communication and interpersonal skills

A high-performing, detail-oriented, and proactive mindset with strong ownership in handling user requests and troubleshooting issues, and driving continuous improvement initiatives

Education:
Bachelor’s or Graduate degree in Computer Science, Engineering, or related field (or equivalent experience)