Data Engineer

• Deep understanding and maintenance of data pipelines: Architect and stabilize transactional data pipelines for the Cisco IT Data team using a unified ingestion framework. 

• Data consumption pipelines: Build and optimize pipelines for analytical reporting, self-service reporting, and data virtualization models tailored to consumer needs. 

• Technical proof-of-concept: Lead POCs with emerging technologies to enhance analytics platforms, ensuring efficiency and alignment with business metrics and canonical data models. 

• AI-driven insights: Define and implement AI/ML use cases (e.g., NLP to SQL conversational agents) to deliver actionable insights and accelerate business growth. 

• Platform enablement: Empower and support multiple Corporate Functional portals (Customer Portal, Partner Portal, etc.) through scalable data analytics platforms. 

• Data quality & governance: Ensure high data quality, consistency, and adherence to governance and security best practices. 

• Collaboration: Work cross-functionally with analytics, engineering, and business teams to deliver end-to-end solutions. 

Minimum Qualifications 

• Bachelor’s degree in engineering, Technology, or a related field. 

• 5-8 years of demonstrated ability in Data Warehousing (DWH) with solid understanding of ETL processes and tools. 

• Expertise in cloud data platforms and database technologies: Snowflake etc. 

• Solid experience with ETL tools: Informatica, DBT. 

• Proficiency with any data quality & observability tools: Snowflake DMF

• Experience with data streaming technologies: Kafka 

• Hands-on scripting and programming skills: Unix shell Script, Python, SQL

• Experience in Enterprise AI integrations: MCP Server, RAG

• Experience in data modeling, designing database structures, and enabling varied data consumption models (virtualization, reporting, etc.). 

• Shown ability to build, optimize, and maintain efficient, reusable, and reliable data pipelines. 

Preferred Qualifications 

• Experience with real-time data and stream processing systems. 

• Familiarity with data visualization tools and techniques. 

• Solid knowledge of data governance, security, and compliance practices. 

• Exposure to AI/ML frameworks and technologies in the data domain (LLMs, predictive/prescriptive analytics, etc.). 

• Experience working in agile teams; SAFe Agilist certification is a plus. 

  • Great teammate, Good communication skills and ability to lead technical initiatives.