Engineering Manager, Data Platform & ML Ops
New
J
JobgetherData Engineering, ML
Based in CanadaFull-TimeManager
Salary not disclosed
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Job Details
- Experience
- At least 2 years of experience managing engineering teams; 5+ years of professional experience in data engineering, ML engineering, or software engineering roles.
- Required Skills
- AWSSnowflakeClickhouseData engineeringSoftware EngineeringBigQueryDatabricks
Requirements
- At least 2 years of experience managing engineering teams focused on data platforms, machine learning, or related technologies.
- 5+ years of professional experience in data engineering, ML engineering, or software engineering roles, preferably within SaaS environments.
- Strong understanding of both data infrastructure and machine learning systems, with the ability to provide technical direction across both areas.
- Experience leading engineers across multiple technical disciplines and supporting high-performing teams.
- Proven ability to deliver reliable data products and platforms with a focus on quality, scalability, and user impact.
- Experience driving technical change and innovation in fast-paced, growing organizations.
- Familiarity with analytical storage technologies such as ClickHouse, Databricks, Snowflake, or BigQuery.
- Experience with ML lifecycle tools, including training pipelines, model serving, and production monitoring.
- Knowledge of cloud-based data and ML infrastructure, particularly AWS environments.
- Strong communication, collaboration, and stakeholder management skills.
Responsibilities
- Lead and mentor a team of engineers working across data platforms and machine learning operations.
- Own the reliability, scalability, and continuous improvement of internal data infrastructure supporting analytics and product initiatives.
- Oversee the complete ML lifecycle, including experimentation, training pipelines, model deployment, and production monitoring.
- Provide technical guidance by contributing to architecture discussions, reviewing solutions, and helping teams make effective engineering decisions.
- Collaborate with data scientists, product managers, analysts, and engineering leaders to turn data and ML investments into measurable outcomes.
- Establish engineering standards, processes, and best practices across data engineering and ML operations.
- Support team development through coaching, feedback, knowledge sharing, and career growth opportunities.
- Drive innovation and continuous improvement within a rapidly evolving technical environment.
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