Data Engineer

The CIA with Data and Analytics organization at Cisco is at the forefront of the company’s AI transformation—building the analytical infrastructure, AI-augmented insight pipelines, and data products that drive customer experience strategy across Cisco’s global portfolio. Our team operates where data engineering, applied AI, and business intelligence converge, and we are actively building a next-generation analytics platform that integrates LLMs, predictive models, and structured data pipelines into a cohesive intelligence layer. The India-based engineering team plays a central role in this build. Engineers here are not supporting AI initiatives—they are building them. The team works on a modern stack spanning Snowflake, dbt, Python, GCP, and Cisco’s AI tooling ecosystem, with direct collaboration with US-based analytics leads and solution architects. This is an environment for engineers who are serious about AI and want to work on production systems, not pilots. Your Impact

As a Data Engineer with an AI Analytics specialization, you will own the design and delivery of AI-augmented data pipelines and analytical systems within the CIA with Data and Analytics organization. You will build the data infrastructure that feeds machine learning models and LLM-based insight pipelines, develop analytical frameworks that surface AI-generated insights to business stakeholders, and contribute to the architectural evolution of Cisco’s customer analytics AI platform. This role requires both data engineering depth and genuine AI curiosity. - Design and build data pipelines in Snowflake and Python that serve as the structured data layer for LLM-based insight generation—including the Dynamic NPS Forecast AI Summary pipeline and similar AI-augmented workflows. - Develop and maintain dbt models that produce clean, well-tested, AI-ready data surfaces—including feature engineering tables, aggregation layers, and prompt-context data structures for generative AI use cases.

- Develop and maintain dbt models that produce clean, well-tested, AI-ready data surfaces—including feature engineering tables, aggregation layers, and prompt-context data structures for generative AI use cases. - Write production-grade Python for data ingestion, API payload processing, LLM API integration, prompt construction, response parsing, and structured output storage. - Integrate with GCP services (Cloud Run, API Gateway, Vertex AI) and Cisco AI tooling to build end-to-end AI data pipelines from raw customer signals to executive-ready insight delivery.

- 4+ years of professional experience in data engineering, analytics engineering, or a closely related role, with demonstrated production ownership of Snowflake environments including schema design, query optimization, and data pipeline reliability. - Intermediate to advanced Python proficiency for data engineering tasks: API integration, JSON payload processing, LLM API calls (OpenAI, Anthropic, or equivalent), structured output parsing, and pipeline automation using pandas, requests, and related libraries. - Expert-level SQL with demonstrated ability to write complex aggregations, window functions, and multi-level hierarchical queries in a Snowflake environment—including performance profiling and optimization.

- Intermediate to advanced Python proficiency for data engineering tasks: API integration, JSON payload processing, LLM API calls (OpenAI, Anthropic, or equivalent), structured output parsing, and pipeline automation using pandas, requests, and related libraries. - Expert-level SQL with demonstrated ability to write complex aggregations, window functions, and multi-level hierarchical queries in a Snowflake environment—including performance profiling and optimization. - Working proficiency in dbt: authoring of incremental models, Jinja macros, test frameworks, and snapshot strategies, with demonstrated understanding of how dbt model quality directly affects downstream analytical and AI pipeline reliability.

Preferred Qualifications - Hands-on experience with GCP AI and data services: Vertex AI, Cloud Run, BigQuery, API Gateway, or Pub/Sub—specifically in the context of end-to-end AI pipeline construction rather than point tool familiarity. - Familiarity with LLM orchestration frameworks (LangChain, LlamaIndex), vector databases (Pinecone, Weaviate, pgvector), or retrieval-augmented generation (RAG) architecture patterns applied to structured enterprise data.

- Design and build data pipelines in Snowflake and Python that serve as the structured data layer for LLM-based insight generation—including the Dynamic NPS Forecast AI Summary pipeline and similar AI-augmented workflows. - Develop and maintain dbt models that produce clean, well-tested, AI-ready data surfaces—including feature engineering tables, aggregation layers, and prompt-context data structures for generative AI use cases. - Write production-grade Python for data ingestion, API payload processing, LLM API integration, prompt construction, response parsing, and structured output storage.

- Collaborate with BI engineers on analytical output surfaces—ensuring AI-generated insights are structured for clean consumption in Power BI or other visualization layers. - Stay at the frontier of AI-native data tooling—Snowflake Cortex, dbt Copilot, LangChain, vector databases, embedding pipelines—and bring forward-looking technical judgment to the team’s architectural decisions. Minimum Qualifications

- Expert-level SQL with demonstrated ability to write complex aggregations, window functions, and multi-level hierarchical queries in a Snowflake environment—including performance profiling and optimization. - Working proficiency in dbt: authoring of incremental models, Jinja macros, test frameworks, and snapshot strategies, with demonstrated understanding of how dbt model quality directly affects downstream analytical and AI pipeline reliability. - Demonstrated experience building or contributing to at least one AI-augmented data pipeline: consuming LLM API responses as structured data, building feature tables for ML models, or constructing prompt-context data layers for generative AI workflows.

- Write production-grade Python for data ingestion, API payload processing, LLM API integration, prompt construction, response parsing, and structured output storage. - Integrate with GCP services (Cloud Run, API Gateway, Vertex AI) and Cisco AI tooling to build end-to-end AI data pipelines from raw customer signals to executive-ready insight delivery. - Build and instrument data quality and observability frameworks that prevent AI pipeline failures from propagating incorrect or hallucinated insights to downstream business consumers.

Objective, gate-level requirements. All five must be demonstrably met. - 4+ years of professional experience in data engineering, analytics engineering, or a closely related role, with demonstrated production ownership of Snowflake environments including schema design, query optimization, and data pipeline reliability. - Intermediate to advanced Python proficiency for data engineering tasks: API integration, JSON payload processing, LLM API calls (OpenAI, Anthropic, or equivalent), structured output parsing, and pipeline automation using pandas, requests, and related libraries.

- Familiarity with LLM orchestration frameworks (LangChain, LlamaIndex), vector databases (Pinecone, Weaviate, pgvector), or retrieval-augmented generation (RAG) architecture patterns applied to structured enterprise data. - Experience with Snowflake Cortex or similar in-warehouse AI capabilities—including ML functions, semantic search, or Cortex Analyst—and awareness of the tradeoffs between in-warehouse AI and external LLM API approaches. - Working knowledge of Power BI sufficient to understand how AI-generated outputs are consumed and displayed at the BI layer, enabling effective collaboration with BI engineers on insight surface design.

- Build and instrument data quality and observability frameworks that prevent AI pipeline failures from propagating incorrect or hallucinated insights to downstream business consumers. - Collaborate with BI engineers on analytical output surfaces—ensuring AI-generated insights are structured for clean consumption in Power BI or other visualization layers. - Stay at the frontier of AI-native data tooling—Snowflake Cortex, dbt Copilot, LangChain, vector databases, embedding pipelines—and bring forward-looking technical judgment to the team’s architectural decisions.

- Experience with Snowflake Cortex or similar in-warehouse AI capabilities—including ML functions, semantic search, or Cortex Analyst—and awareness of the tradeoffs between in-warehouse AI and external LLM API approaches. - Working knowledge of Power BI sufficient to understand how AI-generated outputs are consumed and displayed at the BI layer, enabling effective collaboration with BI engineers on insight surface design. - Familiarity with MLOps or AI pipeline observability practices: model output monitoring, drift detection, prompt versioning, and structured evaluation of LLM output quality in a production context.