Data Analyst

About the team

The Customer Listening & Analytics group within Cisco’s Data and Analytics organization is responsible for the data foundation and analytical systems behind Cisco’s customer experience measurement initiatives. The platform supports NPS tracking, TAC performance analytics, executive business reviews, and customer health scoring across a modern Snowflake and dbt environment with strong integration into GCP and Cisco’s wider data ecosystem.

This is a small, high-ownership team where the expectation is to build, maintain, and continuously improve the core systems that others rely on. The work has visibility at VP and SVP level, and the design choices made here influence the organization broadly. The team is also moving toward an AI-enabled data platform and values people who are excited to help shape that direction.

Role impact

In this Analytics Engineering role, you will take ownership of the architecture, development, and long-term care of the data platform that supports Cisco’s customer experience analytics. The position sits at the intersection of data modeling, pipeline engineering, and AI-supported analytics, with a strong emphasis on clean, scalable, and testable systems. Success in this role depends on working independently, exercising sound technical judgment, and keeping an eye on future AI tooling opportunities.

Key responsibilities

• Lead the design and implementation of dbt models for the Customer Listening platform, including the simplified 6-object structure, parameter-driven control layer, and reusable organizational hierarchy setup.
• Create and tune Snowflake pipelines that support UNIFIED_PARTY_ID resolution, SAV/CAV hierarchy rollups, EBV/EDW reconciliation, and PNPS/TAC metric calculations.
• Develop production-ready SQL and Python code for transformations, automation, and integration with upstream and downstream systems such as GCP services and Mosaic.
• Build strong data quality controls using dbt tests, row-count checks, null checks, and referential integrity validations to keep executive reporting dependable.
• Work with BI engineers on semantic model transitions and investigate data-layer issues that appear in Power BI output.
• Support AI-related pipeline work, including creating tables and structures that enable LLM-generated insights such as the Dynamic NPS Forecast AI Summary pipeline.
• Assess and adopt AI-focused data tools, including Snowflake Cortex, dbt Copilot, and similar capabilities, aligned with leadership’s AI-readiness goals.

Minimum qualifications

• At least 4 years of professional experience in data engineering or analytics engineering, including hands-on ownership of production Snowflake environments, query tuning, RBAC setup, and schema design.
• Strong working knowledge of dbt, with experience in incremental models, macros, Jinja templating, snapshot strategies for SCDs, and dbt test suites.
• Advanced SQL capability, including window functions, recursive CTEs, complex multi-level aggregations, and performance analysis in a cloud warehouse environment.
• Solid Python skills for pipeline scripting, ETL/ELT automation, and light data wrangling with pandas, numpy, or similar libraries.
• Proven ability to design enterprise-scale analytics data architecture, including dimensional modeling, object rationalization, and parameterized configuration layers.

Preferred qualifications

• Familiarity with GCP tools such as Cloud Storage, Cloud Run, BigQuery, and API Gateway, or equivalent Azure services, with the ability to bring cloud-side outputs into Snowflake pipelines.
• Experience using AI outputs in data workflows, such as consuming LLM API responses as structured data, building features for predictive models, or preparing tables for AI summary generation.
• Exposure to Power BI semantic models and the ability to trace and resolve issues originating in the data layer before they surface in reports.
• Hands-on experience with orchestration tools like Airflow, Prefect, or dbt Cloud scheduling, including DAG dependencies and pipeline monitoring.
• Git-based development practices, including branching, pull requests, and CI/CD awareness for dbt or pipeline codebases; experience with data observability tools is a plus.

Why Cisco

Cisco is reimagining how data and infrastructure connect and protect organizations in the AI era and beyond. For 40 years, the company has continued to innovate and build solutions that help humans and technology work together across physical and digital environments. Its products deliver security, visibility, and insight across the full digital footprint.

Backed by deep technology expertise and a global community of builders and specialists, Cisco offers broad opportunities to learn, contribute, and grow. The company emphasizes teamwork, empathy, and large-scale impact. Cisco’s reach is global, and so is the influence of the work done there.