A

Principal Data Engineer

Air-tek
Full-time
On-site
Toronto, Canada
AI
Air-tek is a Canadian-based software company with a powerful suite of unique products that has already achieved a significant share of a huge global opportunity. The product market fit is excellent, and customers are lining up to buy. Although our global customers know us, we intentionally operate in stealth mode during this growth phase.

Our diverse team shares a collective passion for solving complex problems with a drive to innovate and a desire to create the passenger-centric travel industry. Based in Toronto, our inclusive culture is built on trust, collaboration, delivering a great product, and continuous personal development. We love what we do, and we support the team around us.

As our first Principal Data Engineer, you’ll be the cornerstone of our data team, reporting directly to the CTO. You’ll design and build our data platform, pipelines, and integrations, support data products and machine learning initiatives, and manage a small team (3-5 reports) to execute these goals. You’ll help hire and grow the team, ensuring scalable, reliable data solutions that align with our integration-heavy projects. Collaborating with Delivery Leads and product managers, you’ll drive data excellence in a fast-paced environment

Located in Toronto, Ontario, Canada, we work in a hybrid environment (3 days per week) located in the downtown core in an extremely convenient location from Union Station.

Key Responsibilities:

      Technical Leadership:
    • Architect and build a scalable data platform (e.g., data lakes, warehouses) using cloud infrastructure to support enterprise integrations and data products.
    • Develop robust data pipelines for real-time and batch processing, enabling customizations and new product initiatives.
    • Design and implement data integrations with platforms like MuleSoft, ensuring seamless data flows for cross-team projects.
    • Support machine learning initiatives by building pipelines for model training, feature engineering, and data preprocessing, collaborating with future data scientists.
    • Create data products (e.g., dashboards, APIs) to empower internal teams and customers, aligning with product manager requirements.
      • Team Management:
    • Manage a small team (3-5 reports, e.g., junior data engineers, data analysts), delegating tasks, mentoring, and conducting performance reviews.
    • Lead hiring for the data team (4-6 total), defining roles and selecting candidates with complementary skills (e.g., pipeline development, ML ops).
    • Foster team autonomy by empowering reports to own tasks (e.g., pipeline maintenance, dashboard creation) while ensuring alignment with project goals.
      • Collaboration and Coordination:
    • Work within pods alongside Delivery Leads, developers, DevOps, and application specialists, logging milestones and dependencies.
    • Partner with engineering/product and leadership to standardize data requirements for integrations, reducing delays and rework.
    • Collaborate with product managers on data needs for new products, ensuring integration compatibility.
    • Provide technical input during strategic meetings to ensure alignment and technical scalability.
      • Resource and Risk Management:
    • Use resource tools to optimize team capacity and infrastructure allocation, avoiding bottlenecks (e.g., compute shortages for pipelines).
    • Identify and mitigate risks (e.g., data quality issues, integration delays), escalating to Delivery Leads or leadership as needed.

Qualifications:

      Experience:
    • 7-10+ years in data engineering, with expertise in building data platforms, pipelines, and integrations.
    • 2+ years leading small teams (2-5 people), mentoring engineers, and managing hiring/performance.
    • Proven track record delivering scalable data solutions in cloud environments (AWS, GCP, Azure).
    • Experience with integration platforms (e.g., Apache Kafka) and data tools (e.g., Airflow, Snowflake, Databricks, Spark).
    • Familiarity with machine learning pipelines (e.g., feature stores, model training data) is a plus.
      • Skills:
    • Deep knowledge of data architecture, ETL/ELT processes, and real-time/batch processing.
    • Proficiency in programming (e.g., Python, SQL, Scala) and infrastructure-as-code (e.g., Terraform).
    • Strong communication to collaborate with Delivery Leads, product managers, and pods.
    • Leadership skills to delegate, mentor, and foster team autonomy while driving results.
      • Education:
    • Bachelor’s or Master’s in Computer Science, Engineering, or related field (or equivalent experience).
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