Sr. Data Engineer (SSIS to Fabric)

Data Engineering Senior Engineer

Your role is to design, build and deliver enterprise‑grade analytics solutions using Azure and Databricks. You will mentor junior team members, develop technical skills, obtain relevant certifications, and work directly with high‑profile clients to understand their requirements and present solutions.

Key Responsibilities

• Architecture, design, development, and delivery of analytics solutions based on Azure and Databricks technologies.
• Mentor and support the personal development of junior team members.
• Continuously develop technical skills in Azure and Databricks and achieve relevant certifications.
• Engage with high‑profile clients to understand requirements and present solutions to sponsors.

Qualifications

• Demonstrable end‑to‑end experience in Data Engineering, including large‑scale projects.
• Experience with Azure technologies such as Databricks, Synapse, Data Factory, Azure Data Lake Storage, and Cosmos DB.
• Understanding of software engineering tools and concepts; experience in Python, Scala or PySpark.
• Confident communicator able to explain technical terms to non‑technical audiences and mentor colleagues.
• Experience leading small development teams and managing capacity.

AI Application Architect

Lead the design of AI‑powered applications and the integration of autonomous frameworks across cloud platforms.

Key Responsibilities

• Architect AI‑powered applications using autonomous and semi‑autonomous agentic frameworks.
• Apply AI agents across all delivery phases: analysis, design, build, test, deploy, and operations.
• Modernise legacy applications using agentic AI for discovery, transformation, and refactoring.
• Define end‑to‑end architectures integrating traditional software, LLMs, vector stores, and orchestration.
• Drive AI adoption within regions, enabling AI‑driven solution roadmaps.
• Contribute to solution planning and design across multi‑disciplinary and multi‑cloud environments.
• Develop accelerators, reference architectures, and reusable assets for agentic AI patterns.
• Guide engineering, data, and DevOps teams on AI automation and pipeline integration.
• Travel occasionally for high‑value client engagements.

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