Data Engineer

Meet the Team The Growth Metrics Data Domain team with Cisco’s Data and Analytics organization, owns the data infrastructure and analytical frameworks that power Cisco’s strategic vision and incremental growth in recurring revenue streams. Our Data, insights and analytics powers cisco’s renewals, sales and finance organization.

We are AI Native team. We operate as a lean, high-ownership team where engineers are expected to build, maintain, and continuously improve the data foundations that everyone else depends on. Our work is visible at the VP and SVP level, and our architectural decisions have organization-wide impact.

We are architecting the intelligence that drives actionable insights across the entire enterprise. We thrive on a culture of radical collaboration, where diverse perspectives collide to solve complex challenges and push the boundaries of what’s possible.

Your Impact As a Data Analytics Engineer on the Growth Metrics Data Domain, you will own design and development of enterprise-scale Data Engineering and AI solutions ensuring accurate, timely, and high-quality data processing for Cisco’s Unified Revenue Metrics.

You will work at the intersection of data architecture, pipeline engineering, and AI-augmented analytics—building systems that are clean, testable, and built to scale. This role requires independent ownership, strong technical judgment, and a forward-looking mindset toward AI tooling.

Design, build, and maintain scalable and efficient data pipelines to ingest, process, and store large volumes of data from diverse sources. Creating and optimizing robust data models and architectures that support advanced analytics, reporting, and machine learning initiatives.

Write production-grade SQL and Python scripts for data transformation, pipeline automation, and integration with upstream and downstream systems Instrument data pipelines with robust quality frameworks—including dbt tests, row count validation, null assertions, and referential integrity checks—to ensure metric reliability for executive reporting.

Contribute to AI integration workstreams, including building data tables and pipeline structures that support LLM-generated insight delivery. Evaluate and adopt AI-native data tooling—including Snowflake Cortex, dbt Copilot, and related capabilities—in line with the team’s AI future-readiness direction set by VP leadership Strong expertise in Snowflake data warehousing platform and DBT for data transformation and pipeline orchestration.

Proficiency in SQL for data querying, validation, and test case design across Snowflake and Teradata environments. Experience with Python for scripting automation and implementing AI-driven data processes. Minimum Qualifications 4+ years of professional experience in data engineering or analytics engineering, with demonstrated ownership of production-grade Snowflake environments including query optimization, RBAC configuration, and schema design.

Expert-level SQL, including window functions, recursive CTEs, complex multi-level aggregations, and query performance profiling in a cloud data warehouse environment. Intermediate Python proficiency for data pipeline scripting, ETL/ELT automation, and lightweight data wrangling using pandas, numpy, or equivalent libraries.

Demonstrated experience designing data architecture that supports analytical reporting at enterprise scale—including dimensional modeling, object rationalization, and parametric configuration layer design. Preferred Qualifications Working familiarity with building and maintaining Snowflake, DBT -based pipeline Experience incorporating AI outputs into data pipelines—including consuming LLM API responses as structured data, feature engineering for predictive models, or building tables that support AI summary generation workflows.

Exposure to Business Objects, Power BI etc semantic model consumption and ability to diagnose data-layer issues that surface as report-layer errors, enabling clean handoffs with BI engineering counterparts. Experience with pipeline orchestration tools such as Airflow, Prefect, or dbt Cloud job scheduling, including DAG dependency management and pipeline health monitoring.

Git-based development discipline, including branch management, PR workflows, and CI/CD awareness applied to dbt or pipeline codebases; experience with data observability frameworks is a plus. Why Cisco? At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond.

We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint.

Simply put – we power the future. Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you’ll see that the opportunities to grow and build are limitless.

We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere. We are Cisco, and our power starts with you. Why Cisco? At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond.

We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint.

Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you’ll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale.

Because our solutions are everywhere, our impact is everywhere. We are Cisco, and our power starts with you.