Data Solution Architect

Role Overview

We are seeking a Principal AI & Data Engineer of extreme caliber to drive the next generation of data infrastructure, automation, and AI enablement. This is a high-visibility, strategic role designed for a visionary self-starter who operates independently, thrives with minimal direction, and possesses a rare blend of deep technical mastery and sharp business acumen.

In this role, you will not just execute a roadmap—you will build it. You will evaluate our existing data landscape, proactively identify inefficiencies, and pioneer the implementation of cutting-edge Agentic AI and automated solutions across our data pipeline and software development lifecycles. If you are an innovator who can code a Proof of Concept (POC) in the morning and align senior stakeholders on its business value in the afternoon, we want you on our team.

Key Responsibilities

Strategy & Optimization

• Process Assessment: Audit, evaluate, and optimize existing enterprise Data Engineering processes and pipelines to enhance reliability, scalability, and performance.
• Proactive Innovation: Actively spot hidden bottlenecks and structural gaps, translating abstract technical friction into concrete architectural improvements without waiting for top-down direction.
• Stakeholder Alignment: Collaborate effectively across cross-functional business and technical teams, translating complex AI/Data concepts into clear strategic benefits to build organization-wide buy-in.

AI Enablement & Automation

• Agentic AI Pioneers: Architect, build, and deploy custom Agentic AI workflows, autonomous data agents, and LLM-powered solutions specifically tailored to Data Engineering ecosystems.
• SDLC Acceleration: Identify opportunities to embed AI solutions across the entire Software Development Lifecycle (SDLC) to optimize code generation, testing, and continuous deployment.
• Rapid Prototyping: Ideate, develop, and test rapid Proofs of Concept (POCs) for automated systems, successfully scaling validated ideas into robust, enterprise-grade production models.

Required Technical Skills & Qualifications

Cloud & Data Architecture

• Databricks Mastery: Extensive, hands-on experience designing, optimization, and managing large-scale data workflows within the Databricks platform (including Delta Lake, Unity Catalog, and advanced performance tuning).
• AWS Redshift Expertise: Deep technical command over AWS Redshift , including data warehousing architecture, complex query optimization, performance engineering, and cost management.
• Core Data Engineering: Expert-level proficiency in Python, Scala, or SQL, alongside advanced knowledge of modern data pipelining, ETL/ELT architectures, and data orchestration tools.

Artificial Intelligence & Automation

• Advanced AI Implementation: Proven track record of developing AI solutions within data engineering, specifically utilizing Agentic AI frameworks (e.g., LangChain, AutoGen, CrewAI, or semantic layers).
• AI-Driven SDLC: Practical experience leveraging GenAI tools, LLMs, and prompt engineering frameworks to automate software development, code refactoring, and data validation pipelines.

Soft Skills & Leadership Attributes

• High Autonomy: A self-starter capable of navigating ambiguity, operating independently, and delivering high-impact results with minimal hand-holding.
• Elite Problem-Solving: Exceptional analytical thinking with a natural drive to question the status quo and replace manual friction with automated elegance.
• Inspirational Collaboration: Outstanding interpersonal and communication skills, with a proven ability to influence senior business leaders and technical stakeholders alike.

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