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Strong employer brand plus sought-after data/Databricks skills create moderate applicant competition.
Enterprise-scale data architecture and cloud skills are transferable, but require large-scale analytics experience.
Multiple specific mandatory technical requirements (Databricks, GCP, Delta Lake, Airflow, Power BI) increase filter rigidity.
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Design and own end-to-end scalable data and analytics architectures across ingestion, processing, data lakes, warehouses, BI, web apps, and AI-ready solutions.
Architect and optimize data pipelines and workflows ensuring reliability, performance, and standardization across data layers and platforms, primarily Azure and GCP.
Lead AI/ML enablement, including agentic and generative AI solutions, driving adoption, governance, and integration aligned with enterprise cloud strategy.
Strong hands-on experience with Python, Databricks (Delta Lake, Unity Catalog), SQL/PostgreSQL, Apache Airflow, Power BI, and GCP services (Cloud Run, cloud-native architectures).
Proven expertise in designing scalable solutions in GCP and Azure ecosystems, including data integration tools like ADF and multi-cloud interoperability.
Work Experience Required: Not explicitly mentioned in the JD.
Experience in web application architecture with modern backend frameworks (e.g., Node.js, React, Python FastAPI) and AI/ML solution design for data preparation and agentic AI frameworks.
Technical leader capable of end-to-end ownership from architecture design to delivery and adoption of enterprise-scale data and AI products.
Experienced in cloud-native, data-intensive environments with multi-cloud architecture (Azure + GCP) and production-grade software engineering practices (microservices, distributed systems).
Proficient in integrating AI governance, responsible AI adoption, and modern AI/agentic architectures (LangChain, AutoGen, RAG) within enterprise data platforms.