Data & Analytics Engineer

1. Develop and maintain Power BI semantic models, dashboards, and reports.
2. Perform data extraction, cleansing, transformation, validation, and analysis.
3. Build and support data pipelines, integrations, and ETL/ELT processes across enterprise platforms.
4. Assist in developing AI-enabled solutions leveraging LLMs, Generative AI, machine learning, and intelligent automation.
5. Support the development of AI agents, conversational analytics, and agentic workflows that improve productivity and decision-making.
6. Ensure data quality, governance, reliability, and consistency across analytics solutions.
7. Collaborate with business stakeholders to gather requirements and translate them into scalable analytics solutions.
8. Document business processes, data flows, technical specifications, and solution designs.
9. Explore emerging technologies in Data Engineering, GenAI, Agentic AI, ML, and Cloud Analytics to identify business use cases.
10. Contribute to building reusable analytics, automation, and AI assets for enterprise-wide adoption.

The ideal candidate will have foundational experience in analytics, data engineering, Power BI, SQL, cloud data platforms, and emerging AI technologies including Generative AI, Machine Learning (ML), Large Language Models (LLMs), and Agentic AI frameworks. Exposure to Supply Chain, Procurement, Logistics, Manufacturing, or Enterprise Operations analytics is a plus., 1. 1-3 years of experience in Data Analytics, Business Intelligence, Data Engineering, or AI-related projects.

1. Hands-on experience with Power BI development, dashboard design, DAX, Power Query, and semantic modelling.
2. Strong SQL and data analysis skills with exposure to Snowflake, Oracle, Databricks, or modern cloud data platforms.
3. Understanding of data modelling, ETL/ELT pipelines, data integration, and data warehousing concepts.
4. Basic programming experience in Python, SQL, or similar languages.
5. Familiarity with Generative AI platforms such as Azure OpenAI, Microsoft Copilot, OpenAI APIs, or equivalent technologies.
6. Understanding of Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, vector databases, and knowledge search concepts.
7. Exposure to AI/ML concepts including supervised learning, predictive analytics, recommendation systems, and data science fundamentals.
8. Understanding of Agentic AI concepts, AI agents, orchestration frameworks, MCP, tool calling, or workflow automation is desirable.
9. Knowledge of REST APIs, automation technologies, Power Automate, Office Scripts, or similar tools.
10. Understanding of data governance, data quality, security, and metadata management principles.

Analytical & Business Skills

1. Strong analytical, problem-solving, and critical-thinking abilities.
2. Ability to analyse structured and unstructured data to generate actionable insights.
3. Ability to understand business requirements and translate them into technical solutions.
4. Strong attention to detail with a focus on data accuracy and quality.
5. Exposure to Supply Chain, Procurement, Logistics, Manufacturing, or Operations analytics is an added advantage.
6. Curiosity and willingness to learn emerging AI, ML, and data technologies.

Collaboration & Communication

1. Strong verbal and written communication skills.
2. Ability to work effectively in cross-functional business and technical teams.
3. Strong collaboration and stakeholder engagement skills.
4. Ability to manage multiple priorities in a dynamic environment.
5. Ability to communicate technical concepts to business audiences and vice versa., 1. Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, Data Science, Analytics, AI/ML, or a related field.
6. Minimum 2 years of relevant industry experience required.
7. Experience in Data Analytics, Business Intelligence, Data Engineering, Cloud Analytics, AI/ML, or Generative AI solutions preferred.