Data Engineer

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.