Data Engineer

Responsibilities

• Own the end-to-end design and implementation of dbt models, including the simplified 6-object architecture, parametric control layer, and plug-and-play organizational hierarchy configurations.
• Optimize existing data pipelines to improve performances keeping optimal credit utilization without impacting SLA.
• Write production-grade SQL and Python scripts for data transformation, pipeline automation, and integration with upstream and downstream .
• 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.
• Evaluate and adopt AI-native data tooling-including Snowflake CoCo, dbt Copilot, and related capabilities-in line with the team's AI future-readiness direction set by VP leadership.
• Collaborate with other team members to deliver the task at hand to meet the committed timelines.

Requirements

• 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.
• Intermediate to advanced proficiency in dbt, including authoring of incremental models, macros, Jinja templating, snapshot strategy for SCDs, and dbt test frameworks.
• 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, fastAPI, 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.
• Experience in developing pipline using coding assistance such as CoCo,Co-Pilot, Cursor etc.
• Working familiarity with major clod services such as GCP,AWS , with demonstrated ability to integrate cloud-side outputs into a Snowflake-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.
• =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?
• We are Cisco, and our power s

Benefits

Additional Information

Data Reliability Engineer Meet the Team The Customer Asset Data Foundation team with Cisco's Data and Analytics organization, owns the data infrastructure and analytical frameworks that power Cisco's customer assets which includes install base, service agreement and contracts/subscriptions. 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 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 actively building toward an AI-augmented data platform and have a strong bias toward engineers who want to be part of that trajectory.

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Company Intel

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