Data & AI Engineer

About the role

Requirements

• KNOWLEDGE AND SKILLS
• - Hands-on experience as a data engineer with focus on Snowflake, Databricks, Azure, GCP and/or AWS platforms.
• - Strong programming skills in Python, SQL and other relevant languages.
• - Proven experience in developing ETL/ELT pipelines using various technologies, including but not limited to AWS Glue, dbt, Informatica, Talend, Python, SQL, etc.
• - Working knowledge of SAP HANA / SAP HANA Cloud data modelling, XS Engine, Calculation Views, SDI/SDA is a strong plus.
• - Experience with analytical frontend tools (e.g. MicroStrategy, Power BI, Tableau, Spotfire, SAP Analytics Cloud).
• - Exposure to Large Language Models, generative AI, and intelligent agents, including hands-on experience building Agentic AI workflows and automations using tools such as n8n, LangChain / LangGraph, Make (Integromat), Zapier AI, CrewAI, or similar orchestration frameworks.
• - Experience delivering CI/CD and DevOps capabilities in a data environment.
• - A bachelor’s or master’s degree in computer science, Statistics, Mathematics or related.
• - A collaborative, proactive problem solver with strong communication skills and the ability to work effectively across diverse teams.
• - At minimum 5 years of experience in a similar role.

Responsibilities

• Hands-on Engineering & Delivery
• - Develop, test, and maintain robust ETL/ELT pipelines to ingest and transform data from multiple sources into the data lake and data marts.
• - Implement robust web scraping solutions.
• - Optimise performance and cost of data workloads (partitioning, compression, query tuning, etc).
• Data Modelling & Platform Contribution
• - Apply data modelling standards (dimensional models, star/snowflake schemas, canonical data models) to support reporting, analytics, and AI/ML use cases.
• - Contribute to the evolution of the cloud data architecture under the guidance of the Principal Data & AI Analytics, BI, ML & AI Enablement
• Analytics, BI, ML & AI Enablement
• - Work with data scientists, ML engineers, and analytics teams to support the productionisation of ML/AI and LLM-based solutions through reliable data pipelines and feature stores.
• - Expose curated, well-documented datasets to enable self-service reporting, dashboards, and analytical tools.
• Quality, Governance & Security
• - Follow data governance, data quality, and metadata management practices across the platform.
• - Apply security best practices (IAM roles and policies, encryption at rest and in transit, row/column-level security, auditing and monitoring).
• Collaboration & Ways of Working
• - Partner with product, engineering, and business stakeholders to understand data requirements and translate them into scalable technical solutions.
• - Contribute to code reviews, documentation, testing, and DevOps/CI-CD practices, adhering to clean code, reusable components, version control, and observability standards.

Skills

• Snowflake
• Databricks
• Azure
• AWS
• GCP
• Python
• SQL
• ETL/ELT pipelines
• dbt
• Informatica
• Talend
• SAP HANA
• Power BI
• Tableau
• MicroStrategy
• SAP Analytics Cloud
• n8n
• LangChain
• Make (Integromat)
• Zapier AI
• CI/CD
• DevOps