Lead Data Engineer

Job Description

Job Description

Lead Data Engineer

Location : Hybrid, Calgary

Salary : $140,000 - $180,000 CAD

About the Company

Our client is a well-established energy analytics company that has achieved product-market fit and is now in a growth phase.

They are a data-first organization. Data is not a support function here; it is the product. The data engineering team builds revenue-generating data products that customers rely on to make real-world decisions. This is an environment where strong engineers have a direct impact on the business.

About the Role

This role is remote to start, with plans to open an office in Calgary in the near future. For that reason, we’re only considering candidates who already live in Calgary or are open to relocating.

Data is at the core of the company’s platform, and they are hiring a Lead Data Engineer to help anchor and scale a new data engineering team in Canada.

This is a hands-on leadership role. You’ll own key architecture and system design decisions, build and operate high-performance data systems, and help modernize and expand the platform as the company continues its shift toward a more data-centric architecture. You’ll also play a critical role in mentoring engineers, setting technical standards, and driving complex projects forward in a high autonomy environment.

Responsibilities :

• Design, build, and optimize scalable, incremental data pipelines and lakehouse-based data systems.
• Own architecture and system design decisions, with a focus on performance, scalability, reliability, and long-term maintainability.
• Own solutions end-to-end, from design through deployment and ongoing operation.
• Drive technical planning, project execution, and clear communication across teams.
• Develop and orchestrate data workflows using tools like Airflow and modern ELT, ETL patterns.
• Build and maintain distributed data processing applications (Iceberg, Athena, Spark, or similar).
• Implement data governance, data quality, security, and compliance best practices.
• Design efficient storage and retrieval patterns across cloud environments and modern warehouses (S3, Iceberg, Snowflake, Redshift, BigQuery).
• Partner closely with engineering, product, data science, and customer-facing teams to translate data needs into reliable, scalable production systems.
• Monitor, troubleshoot, and continuously improve pipeline and platform performance.
• Provide hands-on mentorship, coach other engineers, and help raise the technical bar across the team.
• Support hiring by interviewing and helping evaluate future data engineering team members.

You Might Be a Great Fit If You…

Preferred

Cultural Fit

You’ll be successful here if you :

If you’re excited to lead, build, and scale real data products with a high-impact team, we’d love to talk.