Senior Manager, Data Engineering
New
J
JumpCloudIT management
Bangalore, India - RemoteFull-TimeManager
Salary not disclosed
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Job Details
- Experience
- 8+ years in applied ML, including several years managing people
- Required Skills
- PythonSQLPyTorchSnowflakeTensorflowdbtDatabricksscikit-learnMLOps
Requirements
- Have 8+ years of experience in applied ML, including several years managing people.
- Bring experience with production MLOps environments, including versioned training pipelines, evaluation gates, a model registry, and automated promotion to serving.
- Have experience managing a team of 8 or more, including performance management and team building.
- Have experience with Salesforce, NetSuite, dbt, Fivetran, Snowflake, and Databricks.
- Demonstrate a good understanding of SQL and strong understanding of software engineering principles and techniques.
- Have a track record of SaaS ownership and a strong focus on reliability principles.
- Have hands-on experience working with agile teams and leading geographically diverse engineering teams.
- Be fluent in Python and scikit-learn, PyTorch, or TensorFlow, and experienced with large-scale data platforms such as Spark or Snowflake.
- Have experience leading ML engineers and data scientists who ship models to production.
- Be familiar with model feature engineering, training, evaluation, serving, monitoring, rollback, and on-call for model health.
- Have experience working effectively with engineering managers and technical and non-technical business stakeholders.
- Have exposure to AI coding agents such as Cursor, Claude, or Copilot, and AI tools such as Gemini or Notebook LLM.
Responsibilities
- Manage and guide the engineering teams responsible for EDA, ESE, and machine learning development.
- Build end-to-end data pipeline architecture using Fivetran ingestion and dbt transformation to move Salesforce and NetSuite data into Snowflake or Databricks.
- Partner with Finance, Sales, and Operations to build reporting models for pipeline health, ARR, billings, churn, and Customer Lifetime Value.
- Enforce analytics engineering practices, including version control, automated dbt testing, and modular data modeling.
- Present financial and operational performance metrics to C-suite leaders using audited data models.
- Provide technical leadership and work with Staff and Principal engineers on architecture, features, best practices, and product quality.
- Lead ML engineers and data scientists in shipping production models and setting standards for feature engineering, training, evaluation, serving, and model feedback.
- Partner with product, platform, and security teams to use identity, device, and telemetry signals to improve model outcomes.
- Hire, onboard, coach, and mentor team members, and coordinate work across L3–L5 individual contributors.
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