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Tier-1 employer, generic Software Engineer title, and broad data skillset elevate candidate competition.
Core data engineering skills transfer across industries, but enterprise DWH and Snowflake experience favors similar organizations.
Explicit 8-10 years plus mandatory Snowflake/DBT/Kafka/Python and DWH expertise tighten shortlisting filters.
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Design, develop, and maintain scalable data processing platforms and transactional data pipelines for Cisco IT Data team using a unified ingestion framework.
Lead proof-of-concept projects with emerging technologies to enhance analytics platforms and implement AI/ML use cases like NLP to SQL conversational agents.
Collaborate cross-functionally to ensure data quality, consistency, governance, and support multiple corporate portals through scalable data analytics platforms.
8-10 years of experience in Data Warehousing (DWH) with expertise in ETL processes and tools.
Bachelor’s degree in engineering, technology, or a related field.
Proficiency with cloud data platforms and database technologies, specifically Snowflake; ETL tools such as Informatica and DBT; scripting with Unix shell, Python, SQL; and data streaming with Kafka.
Experience with data quality & observability tools (Snowflake DMF) and enterprise AI integrations (MCP Server, RAG).
Experienced technical leader capable of architecting and stabilizing complex, reusable data pipelines and platforms in large enterprise environments.
Proven ability to lead technical innovations involving AI/ML integrations and emerging data technologies aligned with business metrics and data governance.
Comfortable working in cross-functional, agile teams with strong knowledge of CI/CD, DevOps tools (Docker, Kubernetes), and system integrations (REST APIs, microservices).