Description
No description provided.
Responsibilities
•Design, develop, and maintain enterprise-grade ETL and ELT pipelines for AI, analytics, reporting, and business use cases
•Create scalable, reliable, and reusable data products from operational, customer, logistics, and business data sources
•Implement data quality controls, validation mechanisms, monitoring, lineage, metadata management, and reliability processes
•Build and optimize data pipelines supporting AI platforms, analytical solutions, and agentic workflow initiatives
•Integrate data across Azure services, Snowflake, APIs, databases, files, and selected AWS platforms
•Collaborate with AI Engineers and Data Scientists to provide high-quality datasets for AI and machine learning initiatives
•Design and implement data models and serving layers that enable efficient data consumption and analytics
•Work with business stakeholders to understand requirements and translate them into sustainable data solutions
•Ensure data governance, security, access control, and compliance requirements are respected across all data assets
•Support onboarding of new enterprise data sources and continuously improve existing data architectures
•Contribute to data platform modernization, automation, observability, and operational excellence initiatives
Requirements
•Fluent English (Mandatory)
•Bachelor's Degree in Computer Science, Data Engineering, Information Systems, Software Engineering, Mathematics, or a related field
•Minimum 4-5 years of experience in Data Engineering, building and maintaining enterprise-scale data platforms, ETL/ELT pipelines, analytical datasets, and cloud-native data solutions
•Strong experience with Python, SQL, data modelling, and enterprise data engineering best practices
•Hands-on experience designing, developing, and maintaining ETL/ELT pipelines in production environments
•Experience building scalable and reliable data pipelines supporting analytics, AI, reporting, and business intelligence use cases
•Strong knowledge of data modelling, data transformation, data integration, and data lifecycle management
•Experience with cloud-native data engineering, preferably within Azure environments
•Practical experience with Snowflake or similar cloud-based data platforms
•Understanding of data quality, monitoring, observability, metadata management, lineage, and governance practices
•Experience integrating data from APIs, databases, file-based sources, enterprise applications, and cloud platforms
•Knowledge of data security, access control, compliance, and governance requirements in enterprise environments
•Ability to work closely with Data Scientists, AI Engineers, Business Stakeholders, and IT teams to deliver data products that support business and AI initiatives
•Experience creating reusable, documented, and production-ready datasets and data products
•Comfortable working in international and cross-functional environments
•Experience using modern AI-assisted development tools such as GitHub Copilot, Cursor, or similar engineering productivity tools
Nice to have
•Experience with AWS data services and multi-cloud environments
•Knowledge of Spark, Databricks, AWS Glue, Apache Iceberg, or similar technologies
•Experience with workflow orchestration and scheduling platforms
•Exposure to AI-related data architectures, feature stores, vector databases, and retrieval pipelines
•Experience supporting GenAI, Machine Learning, or AI-driven applications
•Experience working with logistics, operations, customer, sales, parcel, transport, or supply chain data
Conditions
No conditions specified in the posting.
