Senior Manager, Data Engineering
New
J
JobgetherSaaS data
Fully remote work within IndiaFull-TimeManager
Salary not disclosed
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Job Details
- Languages
- Fluency in written and spoken English is required.
- Experience
- 8+ years of experience in applied machine learning
- Required Skills
- PythonSQLPeople ManagementSalesforceSnowflakedbtDatabricksMLOps
Requirements
- Bring 8+ years of experience in applied machine learning.
- Have several years of people management experience and experience operating production MLOps environments.
- Have managed teams of 8 or more engineers or technical professionals, including hiring, mentoring, and performance management.
- Have experience with versioned training pipelines, evaluation gates, model registries, and automated promotion to serving.
- Have experience with data and analytics technologies such as Salesforce, NetSuite, dbt, Fivetran, Snowflake, and Databricks.
- Demonstrate strong SQL skills and a solid understanding of software engineering principles, architecture, reliability, and production operations.
- Have hands-on fluency in Python and experience with scikit-learn, PyTorch, or TensorFlow, alongside large-scale data platforms such as Spark, Snowflake, or equivalent technologies.
- Have experience leading ML engineers and data scientists who deploy models and manage the lifecycle from feature engineering through monitoring and feedback.
- Understand supervised and unsupervised learning, anomaly detection, classification, ranking and scoring, model evaluation, and imbalanced datasets.
- Have experience owning SaaS products or platforms, with a focus on reliability, operational excellence, and sustainable engineering practices.
- Have experience with agile teams and leading geographically distributed teams in a remote-first environment.
- Have experience with AI-assisted development tools such as Cursor, Claude, or Copilot, and productivity tools such as Gemini or NotebookLM.
Responsibilities
- Lead data engineering, enterprise data applications, and machine learning teams, providing technical direction, delivery oversight, and people leadership.
- Build and operationalize data pipeline architecture that moves CRM and ERP data into centralized platforms such as Snowflake or Databricks.
- Develop unified reporting models with Finance, Sales, and Operations for pipeline health, recurring revenue, billing, churn, and customer lifetime value.
- Establish data governance and analytics engineering standards, including version control, automated testing, modular data modeling, and single-source-of-truth practices.
- Advise senior and executive stakeholders by translating financial, operational, and technical metrics into business insights.
- Lead ML engineering and data science initiatives, moving models from experimentation into reliable production systems.
- Set standards for feature engineering, model training, evaluation, serving, monitoring, rollback, and feedback loops.
- Establish SLAs, monitoring, on-call practices, and operational processes for data and model health.
- Hire, onboard, coach, mentor, and manage team members while supporting performance development and career progression.
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