Lead Data Architect

JOB DESCRIPTION

Role Title

Lead – Data Architecture & AI

Role Summary

The role is responsible for owning and driving the enterprise data & AI strategy, designing and implementing scalable data architecture (data lake, data warehouse, pipelines), and leading the adoption of Analytics, ML, and GenAI to unlock business value. The role will also act as a bridge between business, IT, and advanced analytics teams.

Key Responsibilities

1. Data Architecture & Platform Build

• Design and implement modern data architecture (Data Lake, Data Warehouse, Lakehouse)
• Define data models, standards, governance, and security frameworks
• Build scalable data pipelines (batch + real-time ingestion)
• Ensure high availability, performance, and cost optimization of data platforms

2. Analytics & Visualization

• Enable self-service analytics and reporting platforms
• Lead implementation of BI tools and dashboards
• Partner with business teams to define KPIs, metrics, and insights frameworks
• Drive adoption of data-driven decision making

3. AI / ML / GenAI Initiatives

• Identify and deliver high-impact AI/ML use cases (forecasting, recommendations, optimization)
• Lead development and deployment of machine learning models
• Drive adoption of Generative AI use cases (automation, copilots, knowledge assistants)
• Ensure responsible AI practices including model governance and explainability

4. Data Governance & Quality

• Establish data governance framework (catalog, lineage, ownership)
• Ensure data quality, consistency, and compliance
• Implement master data management (MDM) practices

5. Stakeholder & Program Leadership

• Work closely with business leaders to translate requirements into data solutions
• Lead cross-functional teams (data engineers, analysts, data scientists)
• Manage vendors, partners, and external technology providers
• Drive roadmap execution aligned to business priorities

Key Skills & Expertise

Technical

• Strong experience in Data Platforms (e.g., Snowflake, Databricks, Azure/AWS data stack)
• Expertise in ETL/ELT tools, data pipelines, and data modeling
• Hands-on with BI tools (Power BI, Tableau, etc.)
• Experience in ML frameworks (Python, TensorFlow, PyTorch, etc.)
• Exposure to GenAI / LLM ecosystems

Functional

• Strong understanding of analytics use cases across sales, marketing, supply chain, finance
• Ability to translate business problems into data & AI solutions

Leadership

• Experience in leading data & AI teams or large programs
• Strong stakeholder management and communication skills
• Ability to operate in both strategic and hands-on roles

Experience & Qualifications

• 8–10 years of experience in data engineering, analytics, or AI/ML
• Bachelor's/Master's in Engineering, Computer Science, Data Science, or related field
• Experience in building enterprise-scale data platforms
• Prior experience in AI/ML or GenAI initiatives is preferred