Data Engineer, Digital Transformation & Data

About the role

Reporting to the Data and Analytics Lead, the successful candidate will be responsible for the following: Data Management & Transformation Designing and developing new data pipelines and managing existing data pipelines that extract data from various business applications, databases, and external systems. Implementing data quality checks and validations within data pipelines to ensure the accuracy, consistency, and completeness of data. Transforming data into the desired format by applying data cleansing, aggregation, filtering, and enrichment techniques. Establishing the governance of data and algorithms used for analysis, analytical applications, and automated decision-making. Manage the logical and physical data models to capture the structure, relationships, and constraints of relevant datasets. Ensuring compliance with security and governance best practices. Optimization & Automation Implementing and maintaining continuous integrations and continuous delivery pipelines for deployments and cloud resource provisioning. Optimising data pipelines and data processing workflows for performance, scalability, and efficiency. Optimising models and algorithms for data quality, security, governance, performance and scalability needs. Routinely assessing processor and storage capacity across the data warehouse and extract, transform & load platforms, including capacity planning and forecasting. Monitoring and tuning the data system, identifying and resolving performance bottlenecks and issues, and implementing caching and indexing strategies to enhance query performance. Monitoring the platform for credit consumption and housekeeping. Supporting the deployment and maintenance of AI solutions in the data platform. Collaboration Working with the data lead and business users to manage data as a business asset. Guiding the business users to create and maintain reports and dashboards.

Requirements

• Bachelor's degree in computer science, data science, software engineering, information systems, or related quantitative field; Master's degree preferred.
• At least ten years of work experience in data management disciplines, including data integration, modelling, optimisation, and data quality, or other areas directly relevant to data engineering responsibilities and tasks.
• At least four years of work experience in designing and implementing data architectures in Azure cloud services and Databricks.
• Strong proficiency in Python (PySpark) and SQL programming; experience with Java or Scala is an advantage.
• Experience with relational and non-relational databases, including SQL and NoSQL, is a must, while familiarity with legacy databases such as Oracle is preferred.
• Experience using Azure DevOps, Databricks LakeFlow Jobs for DevOps practices, including version control, CI/CD and pipeline deployment.
• Experience with data catalogue tools, including Unity Catalogue and Microsoft Purview.
• Familiarity with BI & visualisation tools such as Power BI and Python visual packages for data analytics is preferred.
• Experience supporting AI/ML model deployment, feature engineering and production inference is preferred.
• Align with and demonstrate the company's Core Values, which are Integrity, Teamwork, Accountability, Agility, and Ambition.

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Company Intel

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