Senior/ Lead AI Engineer

We are seeking for an Engineer to support AI and data initiatives by building and enhancing the data and analytics foundations that enable customer insights and GenAI-driven use cases. This role focuses on developing scalable data ingestion, transformation, taxonomy, and analytics capabilities that support dashboards, KPIs, trend analysis, and AI-assisted insight generation. The engineer works closely with analytics, engineering, and GenAI stakeholders to translate complex data into reliable, usable, and actionable insights.

Key Responsibilities: 
Data & Analytics Enablement
• Build and maintain datasets, metrics, and analytical views supporting dashboards, KPIs, trend analyses, and drill-down reporting
• Develop and refine semantic layers and metric definitions to ensure clarity, consistency, and usability across analytics products
• Support data visualization and reporting in tools such as Tableau or QuickSight

Data Engineering & Pipelines
• Design and implement automated data ingestion and transformation pipelines for structured and unstructured data sources
• Support taxonomy alignment, labeling, and metadata enrichment to improve discoverability and AI readiness
• Build scalable data storage and transformation layers, including curated datasets and dbt-style models
• Work closely with engineering teams to ensure ingestion correctness, pipeline stability, and data readiness

GenAI & Agentic AI Enablement
• Support integration of analytics pipelines with GenAI and Agentic AI components used for insight generation and exploration
• Apply GenAI tools to improve analytical workflows, automate repetitive tasks, and surface insights more efficiently
• Help prepare datasets and metadata for RAG and AI-driven analytics use cases

Data Quality, Performance & Reliability
• Apply data quality checks, validation logic, and monitoring to ensure accuracy and reliability of analytical outputs
• Support optimization of data pipelines for performance, latency, and scalability
• Contribute to documentation, data definitions, and usage guidance

Collaboration & Problem Solving
• Collaborate with analysts, GenAI specialists, quality teams, and business stakeholders to deliver aligned solutions
• Contribute to technical design discussions and solution reviews
• Navigate ambiguous problem spaces with a structured and solution-oriented approach

Insight Generation & Business Alignment
• Analyze complex datasets to identify relevant takeaways for the executive team
• Validate whether analytical results align with business logic, operational realities, and customer behavior
• Translate business requirements into clear technical specifications so analytical workflows and data outputs support underlying strategic needs

Who are you?
• 3+ years of experience in data engineering, analytics engineering, backend engineering, or data analytics roles.
• Strong SQL skills and experience working with analytical datasets and metric development.
• Hands-on experience with ETL/ELT pipelines, data ingestion frameworks, and API-based integrations.
• Experience working with both structured and unstructured data.
• Familiarity with data visualization tools such as Tableau, QuickSight, or similar.
• Solid understanding of data modeling, data quality, and validation practices.

Nice to have
• Experience supporting or integrating GenAI or Agentic AI solutions within analytics or data platforms.
• Familiarity with cloud-based data architectures (AWS preferred).
• Experience with customer behavior analytics, usage metrics, or quality indicators.
• Exposure to metadata management, taxonomy design, or labeled datasets for AI use cases.

Core Skills & Attributes
• Strong analytical thinking and attention to detail in metrics definition and data interpretation.
• Ability to communicate technical concepts clearly to both technical and non-technical audiences.
• Collaborative mindset with comfort working across teams and disciplines.
• Interest in applying GenAI tools to enhance analytics and data-driven decision-making.