Description
Join the Workforce Productivity Applications team within Cisco IT, where we support all third-party enterprise applications.
As a Productivity Data Engineer & Strategist, you will develop and implement data architecture to store, process, analyze, and visualize large, high-volume datasets.
Your responsibilities will include:
- Data Architecture: Develop and implement data architecture to store, process, analyze, and visualize large, high-volume datasets.
- Technical Guidance: Guide technical peers on database and data lake sources, as well as security requirements such as RBAC/ABAC, row- and column-level security, masking/tokenization, key management, PII classification.
- Insights & BI: Translate complex data structures into actionable insights using Business Intelligence and analytics tools such as Tableau or Power BI including building organization-wide dashboards for tracking measurements, usage and KPI’s for email, SaaS applications, and productivity suites.
- Collaboration: Collaborate with engineers and business stakeholders to understand, articulate, and resolve business problems, alongside related security and privacy concerns.
- AI-Enabled Analytics: Design semantic models and certified datasets that make natural-language and AI-assisted BI reliable - Power BI Copilot, Microsoft Fabric, Tableau Agent/Pulse or Snowflake Cortex - including the measure definitions, field descriptions, and synonyms these tools depend on to return trustworthy answers.
The ideal candidate will have:
- Education & Experience: Bachelor’s degree with 7+ years of related experience, Master’s degree with 4+ years of related experience, or Ph.D. with 1+ year of related experience.
- Data Modeling: Proven capability in enterprise data modeling (dimensional, relational, and NoSQL) and the design of robust data warehouses and data lakes.
- Technical Proficiency: Proficiency in core programming and querying languages essential for data orchestration and analytics, primarily Python, SQL, dbt, and Snowflake.
- Core Competencies: Excellent communication and strategic planning skills, combined with a self-motivated approach and a strong focus on execution.
- Data Pipelines: Demonstrated ability to ensure data quality while creating and maintaining optimal data pipelines.
Preferred qualifications include advanced pipeline engineering, reporting and visualization experience, and hands-on experience enabling and tuning natural-language/AI features in a BI platform.