Senior Manager, Data Engineering

New

JumpCloudIT management software

Remote within the country noted in the Job Description.Full-TimeManager

Salary not disclosed

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Job Details

Languages: You will be required to speak and write in English fluently.
Experience: 8+ years in applied ML, including several years managing people
Required Skills: PythonSQLPyTorchSnowflakeTensorflowdbtDatabricksscikit-learnMLOps

Have 8+ years of experience in applied ML, including several years managing people.
Have experience managing a team of 8 or more, conducting performance management, and building a successful team.
Have experience with production MLOps environments, including versioned training pipelines, evaluation gates, a model registry, and automated promotion to serving.
Have experience with Salesforce, NetSuite, dbt, Fivetran, Snowflake, or Databricks.
Have good understanding of SQL and strong understanding of software engineering principles and techniques.
Have hands-on fluency in Python and scikit-learn, PyTorch, or TensorFlow, and experience with large-scale data platforms such as Spark or Snowflake.
Have a track record of SaaS ownership and a strong understanding of reliability principles.
Have hands-on experience working with agile teams and leading geographically diverse engineering teams.
Be able to work 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.
Be able and willing to participate in assigned on-call shifts.
Be located in and authorized to work in India.

Build end-to-end data pipeline architecture using Fivetran, dbt, Salesforce, NetSuite, and 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 stakeholders using audited data models.
Lead and guide engineering teams, working with Staff and Principal engineers on features, architecture, best practices, and product quality.
Lead ML engineers and data scientists building and shipping models to production.
Set standards for feature engineering, training, evaluation, serving, and model feedback loops.
Partner with Product, Platform, and Security to use identity, device, and telemetry signals to improve model outcomes.
Hire, onboard, coach, and sequence work across engineering team members, with clear SLAs, monitoring, rollback, and on-call for model health.

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