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 Vice President, Data Engineering, you’ll be the cornerstone of our data
team, reporting directly to our Founder/CEO. You’ll lead the charge in designing
and building our data platform, pipelines, and integrations, support data
products and machine learning initiatives, and build/manage the team that will
execute these goals. You’ll lead the charge in hiring and growing the team,
ensuring scalable, reliable data solutions that align with our integration-heavy
projects. Collaborating closely with engineering, customer delivery and product
management, you’ll drive data excellence in a fast-paced, high growth
environment.
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Key Responsibilities
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Technical Leadership:
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Build and maintain the systems that handle data including pipelines,
databases, data warehouses and ensuring data quality and accessibility.
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Design and implement data storage solutions, creating ETL processes, managing
data infrastructure, and ensuring data is available and reliable for
analysis.
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Architect and build a scalable data platform (e.g., data lakes, warehouses)
using cloud infrastructure to support enterprise integrations and data
products.
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Develop robust data pipelines for real-time and batch processing, enabling
customizations and new product initiatives.
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Design and implement data integrations with platforms like MuleSoft, ensuring
seamless data flows for cross-team projects.
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Support machine learning initiatives by building pipelines for model
training, feature engineering, and data preprocessing, collaborating with
future data scientists.
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Create data products (e.g., dashboards, APIs) to empower internal teams and
customers, aligning with product manager requirements.
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Team Management:
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Build the team from scratch, defining roles and selecting candidates with
complementary skills (e.g., pipeline development, ML ops).
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Manage your team (data engineers, data analysts), delegating tasks,
mentoring, and conducting performance reviews.
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Foster team autonomy by empowering reports to own tasks (e.g., pipeline
maintenance, dashboard creation) while ensuring alignment with project goals.
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Collaboration and Coordination:
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Work within pods alongside customer delivery, developers, DevOps, and
solution architects; logging milestones and dependencies.
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Partner with engineering/product and leadership to standardize data
requirements for integrations, reducing delays, and rework.
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Collaborate with product managers on data needs for new products, ensuring
integration of compatibility.
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Provide technical input during strategic meetings to ensure alignment and
technical scalability.
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Resource and Risk Management:
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Use resource tools to optimize team capacity and infrastructure allocation,
avoiding bottlenecks (e.g., compute shortages for pipelines).
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Identify and mitigate risks (e.g., data quality issues, integration delays),
escalating data governance, security, compliance and performance risks or
issues to key internal colleagues.
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Skills:
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Deep knowledge of data architecture, ETL/ELT processes, and real-time/batch
processing.
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Proficiency in programming (e.g., Python, SQL, Scala) and
infrastructure-as-code (e.g., Terraform).
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Strong communication to collaborate with Delivery Leads, product managers,
and pods.
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Leadership skills to delegate, mentor, and foster team autonomy while driving
results.
Experience & Education
- 7-10+ years in data engineering, with expertise in building data platforms,
pipelines, and integrations.
- 4+ 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.
- Bachelor’s or Master’s in Computer Science, Engineering, or related field (or
equivalent experience).
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