Data & AI Engineer

Responsibilities:

• Design and build end‑to‑end data pipelines, including data collection, transformation, quality controls, and integration layers, to support enterprise data and analytics solutions.
• Partner with solution design and business teams to clarify data needs and assemble large, complex datasets that meet functional and technical requirements.
• Develop and continuously refine analytics solutions that align with both business objectives and technical constraints.
• Work alongside the data architect to ensure consistency, integrity, and governance of data models across systems.
• Build and maintain scalable infrastructure for efficient extraction, transformation, and loading (ETL/ELT) of data from diverse sources.
• Develop data tools and utilities that help analytics and data science teams build, test, and optimise their models and analytical workflows.
• Collaborate with DevOps engineers to support stable, reliable operation of data and analytics platforms and services.
• Partner with data analysts and data scientists to design and implement APIs that connect data layers with downstream applications and models.

Requirements:

• Bachelor's or Master's degree in a relevant discipline such as computer science, information technology, or data science.
• Minimum 3 years of hands‑on experience with SQL/PostgreSQL, data integration, and BI solutions, including integration with third‑party tools.
• Experience with application server environments (e.g. ERP), as well as big‑data tech such as Spark, Scala, Python, SQL scripting, relational databases, and NoSQL platforms (e.g. HBase, MongoDB, Cassandra).
• Solid exposure to cloud technologies (e.g. Azure), including Data Lake, Databricks, data factories, and BI dashboarding tools.
• Proven track record of working with large and complex datasets and building full‑lifecycle data pipelines on on‑premise or cloud data platforms.
• Practical experience coding in data management, data warehousing, or unstructured data environments.
• Experience using AI‑assisted coding tools (e.g. GitHub Copilot) to enhance development speed and code quality.
• Familiarity with modern GenAI concepts (e.g. RAG), orchestration frameworks such as LangChain and LlamaIndex, and vector databases, to support collaboration with data scientists and ML engineers on AI use cases.
• Demonstrated experience with Azure cloud platforms and related technologies.
• Ability to define and implement robust data integration patterns and end‑to‑end pipelines.
• Strong understanding of data modelling, data warehousing, and BI concepts, with an emphasis on reusability and consistency.
• Self‑motivated, comfortable working independently, and highly detail‑oriented.
• Curious and proactive about emerging AI and data‑engineering trends and their practical application in enterprise environments.