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Popular senior data engineering title, mid-level experience band, and metro hiring increase candidate competition.
Core data engineering skills are transferable but hospitality/financial analytics experience is preferential.
Explicit 5+ years plus mandatory Python/SQL/dbt/GCP/Databricks experience makes filters stringent.
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Design, develop, and maintain scalable, automated data pipelines managing diverse domains including financial, payroll, point-of-sale, marketing, and purchase analytics.
Operate data platforms across Google Cloud (GCP Gemini Enterprise) and Databricks within a multi-cloud architecture, supporting dbt-led ETL pipelines and AI-agentic workflows with knowledge graphs/semantic layers.
Implement data quality, governance frameworks, and troubleshooting; contribute to product roadmap and documentation for reproducible engineering processes.
5+ years of experience in data engineering or analytics, including senior or technical-lead responsibilities.
Proficiency in Python and SQL for production data pipeline development and operations.
Experience with dbt-led ETL, multi-cloud environments especially Google Cloud and Databricks platforms.
Bachelor’s or Master’s degree in data science, mathematics, statistics, or computer science.
Experienced in designing and operating data pipelines in cloud and multi-cloud environments with emphasis on automation and reliability.
Ability to integrate complex data sources into intuitive models enabling downstream analytics and business outcomes.
Familiarity or strong interest in AI-agentic workflows, knowledge graphs, ML techniques (particularly text analysis and classification), and a design-led approach to data solutions.