Senior Manager, Data Engineering
New
Z
ZscalerCybersecurity
Remote - USAFull-TimeManager
Salary196,000 - 245,000 USD per year
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Job Details
- Experience
- Five or more years of experience leading, mentoring, and growing high-performing data engineering, platform engineering, or analytics engineering teams
- Required Skills
- PythonSQLGitSnowflakeCI/CDdbt
Requirements
- Five or more years of experience leading, mentoring, and growing high-performing data engineering, platform engineering, or analytics engineering teams.
- Demonstrated experience driving technical strategies to transition data structures into AI-ready units, alongside hands-on knowledge of building Retrieval-Augmented Generation (RAG) and Graph RAG architectures.
- Deep architectural and operational knowledge of Snowflake and dbt, including analytics engineering, modeling standards, and testing.
- Practical experience implementing or operating within a self-service data platform environment and a Data Mesh architectural framework.
- Strong hands-on experience with cloud-native ETL/ELT tools, preferably Matillion Data Productivity Cloud or similar.
- Solid software engineering foundations in Python and SQL, including CI/CD, Git, and automated testing.
- Ability to act as an owner with a strong bias for action, navigating between high-level strategy and hands-on execution.
Responsibilities
- Drive platform strategy and architecture by defining the technical roadmap and long-term vision for the Enterprise Data Platform utilizing Snowflake, dbt, Matillion DPC, and Streamlit.
- Balance platform evolution and business support, transitioning teams toward a modern platform operating model with medallion and mesh architectures while supporting critical organization-wide data initiatives.
- Embed AI into data and analytics by designing pipelines, analytics models, and Retrieval-Augmented Generation systems to transition central data and federated BI teams into AI-ready units.
- Enable self-service and mesh architecture to empower decentralized business functions to independently build custom data models across foundational analytics layers.
- Implement robust platform governance, observability, and cost attribution frameworks while executing hands-on technical troubleshooting, prototyping advanced workflows, and developing AI/LLM-powered AIOps systems for operational efficiency.
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