Data Engineer

Data Engineer (Hybrid)This role is listed as hybrid the selected applicant should be expected to come into office 3 days a weekNOTE: Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received.We are the Customer Asset Data Foundation team within Cisco's Data and Analytics organization. We lead the data infrastructure and analytical frameworks powering Cisco's customer assets—install base, service agreements, and contracts/subscriptions. Our insights fuel renewals, sales, and finance teams. We operate as a lean, high-ownership, AI-native team, with work that impacts the entire organization and is visible at the highest levels. We're excited to be building an AI-augmented data platform and invite engineers who want to be part of this journey!Your ImpactAs a Data Reliability Engineer, you will design, develop, and maintain AI-native data pipelines that are scalable, testable, and built for the future. We expect you to take full ownership of your work, applying strong technical judgment and a forward-thinking outlook toward AI tooling. Together, we will:Lead the build and implementation of dbt models, including architecture and organizational configurations.Optimize data pipelines to improve performance and cost-efficiency while meeting SLAs.Write production-grade SQL and Python scripts for data transformation, automation, and integration.Build and maintain data quality frameworks to ensure reliable metrics for executive reporting.Support AI integration by crafting data structures that enable LLM-generated insights.Evaluate and adopt AI-native tools like Snowflake CoCo and dbt Copilot to stay ahead.Collaborate closely with teammates to deliver on commitments and drive impact.We lead projects from design through completion, using our knowledge of Cisco data sources and security requirements. We validate, transform, and index data using analytic techniques and business insights. We analyze correlations across data sets and conduct statistical analyses to uncover meaningful patterns. We clean and prepare data for scalable use and develop custom data models with minimal guidance.We build and optimize big data pipelines, develop new engineering features, and write clean, functional code with unit tests. We perform debugging, troubleshooting, and code reviews, always applying secure coding principles. We contribute to data quality, compliance, and automation frameworks, using internal security tools to flag and call out issues. We design monitoring features to supervise pipeline quality and performance. We work hand-in-hand with data scientists to support business needs and communicate findings through reports and visualizations.Minimum QualificationsWe want you to bring:4+ years of shown experience in data or analytics engineering, owning production-grade Snowflake environments with query optimization, RBAC, and schema design.Intermediate to sophisticated skills in dbt, including incremental models, macros, Jinja templating, snapshot strategies, and test frameworks.Expert-level SQL proficiency with window functions, recursive CTEs, complex aggregations, and query profiling in cloud data warehouses.Intermediate Python skills for pipeline scripting, ETL/ELT automation, and data wrangling using libraries like pandas or FastAPI.Experience designing enterprise-scale data architectures that support analytical reporting, including dimensional modeling and parametric configuration.