Data Engineer

The Customer Listening & Analytics team is part of Cisco’s Connected Engineering and Technology Organization (CETO) and operates as the data backbone for Cisco’s global customer experience measurement programs. We own the data infrastructure, transformation pipelines, and analytical frameworks that power NPS reporting, TAC performance analytics, executive business reviews, and customer health scoring—working at scale across Cisco’s global install base.

Our India-based data engineering team is a core delivery engine, not a support function. Engineers here own production workstreams end-to-end and work in close collaboration with senior architects and analytics leads based in the US. The team operates on a modern data stack (Snowflake, dbt, Python, GCP) and is actively building toward an AI-augmented data platform aligned to Cisco’s enterprise AI transformation agenda.

As a Data Engineer on the Customer Listening team, you will own the development, maintenance, and continuous improvement of data pipelines and transformation models that serve executive-level analytics and AI-driven insight delivery. You will work within a well-architected Snowflake/dbt environment, contributing to data quality, pipeline reliability, and architectural evolution as the team scales its AI capabilities.

• Build and maintain dbt models supporting NPS measurement, TAC case analytics, EBV/EDW reconciliation, and PNPS computation—including incremental models, snapshot strategies, and macro-driven parametric configurations.
• Write production-grade SQL in Snowflake for multi-level hierarchy resolution (SAV → CAV → UNIFIED_PARTY_ID), complex aggregations, and pipeline performance optimization.
• Develop Python scripts for data ingestion, ELT automation, API payload processing, and lightweight data wrangling tasks within the Customer Listening pipeline ecosystem.
• Instrument pipelines with robust data quality frameworks—including dbt tests, row count assertions, null checks, and referential integrity validations—to ensure metric reliability for VP-level reporting.
• Collaborate with BI engineers on semantic model handoffs, diagnosing and resolving data-layer issues that manifest as reporting errors in Power BI.
• Support AI integration workstreams by building well-structured data layers that feed LLM-generated insight delivery pipelines (e.g., Dynamic NPS Forecast AI Summary).
• Contribute to the team’s AI future-readiness direction by evaluating and adopting AI-native data tooling including Snowflake Cortex, dbt Copilot, and related capabilities.

Objective, gate-level requirements. All five must be demonstrably met.

• 4+ years of professional experience in data engineering, with demonstrated production ownership of Snowflake environments including schema design, query optimization, RBAC configuration, and cost governance.
• Intermediate to advanced dbt proficiency: authoring of incremental models, Jinja macros, snapshot strategies for slowly changing dimensions, generic and singular test frameworks, and dbt documentation practices.
• Expert-level SQL including window functions, recursive CTEs, lateral flattens, multi-level hierarchical aggregations, and query profiling in a cloud data warehouse setting.
• Intermediate Python proficiency for ELT scripting, data wrangling (pandas, numpy), and API payload ingestion—with the ability to build and maintain pipeline scripts independently.
• Demonstrated experience designing and rationalizing data models at enterprise scale, including dimensional modeling, object consolidation, and configuration-driven architecture patterns.

• Experience integrating AI outputs into data pipelines—consuming LLM API responses as structured data, building tables that support AI summary generation workflows, or feature engineering for predictive analytics.
• Familiarity with AI-native data tooling such as Snowflake Cortex, dbt Copilot, or similar capabilities; awareness of where these tools add value and where human oversight remains essential.
• Working knowledge of Power BI semantic model consumption—sufficient to diagnose data-layer issues that surface as BI report errors and enable clean handoffs with BI engineering counterparts.
• Exposure to GCP services (Cloud Storage, Cloud Run, BigQuery, API Gateway) or Azure equivalents, with ability to integrate cloud-side outputs into a Snowflake-based pipeline.
• Git-based development discipline, pipeline orchestration experience (Airflow, Prefect, or dbt Cloud scheduling), and data observability practices (Great Expectations or equivalent).

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. 

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