Role Description:
* About the Role: We are seeking a seasoned, hands-on Lead Data & AI Solutions Architect with deep expertise in large-scale, complex data ecosystems.
* In this role, you will sit at the intersection of enterprise data architecture, rapid solution prototyping, and GenAI-driven analytics.
* You will partner directly with business stakeholders to translate strategic problems into high-performance data architectures, modernizing reporting pipelines through AI-powered interfaces.
Key Responsibilities:
Candidate must have Apple Exp:
Data Architecture & Pipeline Engineering:
* Architect, build, and optimize high-throughput, low-latency data pipelines handling large-scale and complex datasets.
* Design analytical models and transformation layers utilizing Snowflake, SingleStore, and DBT.
* Ensure high performance, data integrity, and cost efficiency across distributed architectures.
Business Engagement & Rapid Prototyping:
* Engage directly with senior business leaders to understand strategic needs, translate ambiguity into technical roadmaps, and convert ideas into production-ready POCs rapidly.
* Lead end-to-end technical delivery from discovery through architecture, implementation, and handover.
AI-Powered Reporting & Modern Analytics:
* Build next-generation reporting solutions that integrate AI/LLMs (e.g., automated narrative generation, natural language to SQL/analytics, dynamic summarization).
Design APIs and data consumption layers to serve operational dashboards and interactive analytics applications. Qualifications & Skillsets Must-Have:
* Big Data & Data Warehousing: 8+ years of hands-on data engineering/architecture experience with massive, complex data sets. Deep expertise in Snowflake; strong experience with SingleStore (or high-concurrency real-time databases) highly preferred.
* Modern Data Stack: Expert-level data modeling and transformation workflows using dbt and robust orchestrators/pipelines.
* Stakeholder & Business Interaction: Proven ability to work directly with business stakeholders, gather requirements, present technical solutions, and iterate quickly based on feedback.
* AI-Enabled Analytics: Demonstrated capability in leveraging AI/LLM tooling to power reporting, intelligent data extraction, or conversational analytics interfaces.
* Core Languages: Advanced SQL and Python
