Data Engineering Manager
PayZen
AirflowAmazon RedshiftCloudKafkaPythonSQL
Salary
💰 CA$169,200 - CA$228,900 per year
Job Level
Mid-LevelSenior
Tech Stack
AirflowAmazon RedshiftCloudKafkaRaySparkSQL
About the role
- Lead a team of data engineers and report to the Director of Data.
- Partner with the team’s Technical Program Manager to prioritize initiatives and collaborate on building quarterly roadmaps.
- Manage team performance, support individual growth, ensure high-quality deliverables, and scale the team as needed.
- Shape technical decisions in collaboration with technical leads, principals, and distinguished engineers.
- Own the strategy, roadmap, and delivery of high-performance, scalable, and cost-efficient data infrastructure including data stores, compute engines, and orchestration systems.
- Ensure data systems are resilient, observable, and governed, implementing recovery strategies, proactive monitoring, and best practices for security, integrity, and compliance.
- Partner across engineering, analytics, and go-to-market teams to deliver well-structured, high-quality product data and build tools, automation, and solutions.
- Drive innovation and efficiency with the use of AI tools to support the data strategy and enable continuous exploration and improvement.
Requirements
- Proven experience managing engineering teams - ideally in data platform, and/or software engineering domains - with a track record of delivering high-quality software and data solutions.
- A strong technical foundation in software and data engineering, including distributed data systems, orchestration frameworks, cloud infrastructure, performance tuning, scaling strategies, and cost optimization.
- Hands-on experience in systems design, SQL, modern data tools, and data best practices, including modeling, governance, and quality management.
- Experience implementing observability frameworks, SLAs, disaster recovery strategies, and other practices to ensure resilient, reliable, and compliant data systems.
- The ability to lead and adapt in an agile environment, fostering a culture of continuous learning, critical thinking, and creative problem-solving.
- Excellent collaboration and communication skills, with the ability to work cross-functionally with engineering, product, analytics, and data science teams while mentoring and coaching direct reports.
- Strategic thinking and roadmap planning capabilities, with experience shaping infrastructure initiatives that have measurable impact.
- Strong leadership and mentorship skills, using your experience to guide, influence, and provide constructive feedback to direct reports—ensuring their growth and enabling the team to exceed its goals.
- Highly desired: Hands-on experience with modern data stack tools such as Redshift, Trino, dbt, Airflow, Kafka, and familiarity with data processing frameworks such as Spark and Ray.
- Highly desired: Background in building internal developer platforms, self-service data tooling, or workflow automation for data teams.
- Highly desired: Sound understanding of lambda and/or kappa architecture, batch and streaming principles and experience implementing either of the two architectures in a production environment.
- Highly desired: Experience in working with Engineering teams to influence upstream data design and instrumentation.
- Highly desired: Exposure to data science and machine learning workflows and their infrastructure requirements.
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