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# Senior Data Engineer, Network Platform

- **Company:** Cisco Systems, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Query Performance, Artificial Intelligence, Airflow, Automation of Tests, Software Bug Management, Continuous Integration, Information Engineering, Data Security, Distributed Computing Environment, Python (Programming Language), PostgreSQL, Operational Databases, Unstructured Data, Sql Optimization, Snowflake, Apache Spark, Data Management, Data Pipelines, Cisco
- **Published:** November 9, 2026
- **Apply:** https://www.juju.com/job/28_25197_0a291441dae89e81c59837e84b4729c7

## About the Role

* Bachelor's degree + 7 years of related experience, or Masters 3+ years of related experience, or PHD +1 year of related experience
 * 5+ years advanced SQL including data modeling, complex transformations, and query performance tuning.
 * 5+ years Python building production data pipelines/services with automated testing and maintainable code.
 * 3+ years of Snowflake building, optimizing, and operating production data workloads (Postgres experience is a plus).
 * Experience with distributed processing and orchestration (e.g., Spark and Airflow/Glue or equivalent) plus engineering practices across CI/CD, access controls, data quality, observability, incident response, or cost management., * Experience building trusted data context layers (metric semantics, lineage, quality/freshness ownership, governed access) integrated with catalog/governance systems.
 * Experience applying AI tools/agents to scoped data engineering work (pipeline validation/bug triage), including evaluating outputs and grounding them in trusted context while managing data access and operational risk.
 * Demonstrated ability to influence architecture across teams and communicate trade-offs while mentoring engineers and growing technical capability.

## Description

The Network Platform Data Science team helps turn Cisco's rich, multifaceted customer and network data into smarter decisions, improved customer experiences, and faster innovation. You'll partner across Engineering, Product, Security, UX, Finance, Licensing, and other teams to deliver data platforms and products that are reliable, governed, and ready for AI to use accurately. The environment is at massive scale (10 PB+ across 30+ systems) and blends data engineering rigor with trusted data context-so teams can move from "data exists" to "data is trusted and actionable."

Your Impact

You will set technical direction for reliable, scalable batch and streaming pipelines and data products across structured and unstructured data.

You will define and continuously improve expectations for data quality, freshness, availability, observability, recovery, and cost, then lead production incident investigations to deliver lasting fixes.

You will build and evolve a trusted, machine-readable data context layer-covering business definitions, metric semantics, ownership, lineage, quality/freshness, and governed access integrated with existing catalog/governance systems.

You will apply AI and agentic workflows to well-scoped data engineering needs (pipeline development, validation, and bug triage), including evaluating results and using bounded permissions with human review for production-changing actions.

Success means the organization has a trusted data foundation that scales, is observable and recoverable, reduces "definition drift," and helps teams-including AI applications-use data accurately and confidently.