Data Engineer

Description

The company has a $30+ billion business in technology and services and serves 75% of the Fortune Global 100.

It has experts in more than 70 countries and is a leader in AI and digital infrastructure, covering enterprise AI, cloud, security, connectivity, data centers and application services.

The parent group invests over $3 billion a year in R&D.

You would join the Data & AI practice and work with enterprise customers on cloud data platforms that turn data into analytics, reporting and AI results.

The work centers on Microsoft Azure and Microsoft Fabric, with pipelines, data models and lakehouse solutions.

Responsibilities

• Create and run scalable data pipelines and ELT and ETL processes.
• Build and look after data platforms on Microsoft Fabric and Azure services.
• Deliver Lakehouse, Data Warehouse and Data Factory solutions in Microsoft Fabric.
• Connect cloud and on-premises data sources through integration solutions.
• Shape data models that serve analytics, reporting and AI work.
• Write transformation logic in SQL, Spark and Python.
• Put data quality, governance and security controls in place.
• Track platform performance, reliability and cost, and improve them.
• Work with architects, analysts, data scientists and business stakeholders to deliver data-driven outcomes.
• Help adopt CI/CD and automated deployment practices.

Requirements

• Essential: 5+ years in data engineering, data warehousing or cloud data platform development.
• Essential: solid experience with Azure Data Factory, Azure Data Lake Storage, Azure Synapse Analytics, Azure SQL, Azure Databricks and Azure DevOps.
• Essential: hands-on work with Microsoft Fabric, including Data Factory, Lakehouse, Warehouse, OneLake, Power BI and Notebooks.
• Essential: strong SQL and data modelling skills.
• Essential: experience building pipelines with Python and/or PySpark.
• Essential: understanding of warehousing concepts, including star and snowflake schemas.
• Essential: experience working in Agile delivery environments.
• Essential: strong stakeholder engagement and problem-solving skills.
• Desirable: experience with Snowflake data platforms.
• Desirable: experience with Databricks Lakehouse architecture.
• Desirable: experience implementing data governance solutions such as Microsoft Purview.
• Desirable: exposure to real-time or streaming data architectures.
• Desirable: experience in consulting or professional services environments.
• AI & Emerging Technologies (Advantageous): experience integrating AI and Generative AI capabilities into data platforms.
• AI & Emerging Technologies (Advantageous): knowledge of Azure AI Services, Azure OpenAI, Microsoft Copilot Studio or Microsoft Fabric AI capabilities.
• AI & Emerging Technologies (Advantageous): experience supporting data foundations for machine learning or AI workloads.
• AI & Emerging Technologies (Advantageous): understanding of vector databases, Retrieval-Augmented Generation (RAG) and modern AI architectures.
• AI & Emerging Technologies (Advantageous): Microsoft Azure certifications are highly regarded, such as Azure Data Engineer Associate (DP-203), Fabric Analytics Engineer Associate (DP-600) and Azure Fundamentals (AZ-900).

Conditions

The company values diversity and inclusion and wants everyone to grow and belong.