At Vodafone, we’re not just shaping the future of connectivity for our customers – we’re shaping the future for everyone who joins our team. When you work with us, you’re part of a global mission to connect people, solve complex challenges, and create a sustainable and more inclusive world. If you want to grow your career whilst finding the perfect balance between work and life, Vodafone offers the opportunities to help you belong and make a real impact.

Responsibilities or duties

1.Architect, develop, and operationalize big data applications, and end-to-end data pipelines that transform raw data into high quality, query ready datasets. The role focuses on designing scalable ETL/ELT workflows and robust data architectures that support the rapid deployment of new business use cases, ensuring that data is consistently integrated, modeled, and accessible across data platforms.

2.Drive the technical implementation of data governance, security, and quality standards within the data lifecycle. By building automated validation frameworks and optimizing storage and ingestion strategies, this role ensures the accuracy, lineage, and privacy of data, providing the dependable foundation required for reporting, advanced analytics, machine learning, and real time decision making.

Qualifications or requirements (e.g., education, skills)

•Bachelor’s degree in Computer engineering, Computer Science, Information Technology, or a related field.

Experience needed

•2+ years of experience in Data Engineering, Big Data Development, or Data Architecture, specifically focused on building and deploying production-grade data pipelines.

•Experience within Telecommunications or a similarly data intensive industry

Any other provided details (e.g., benefits, work environment, team info, or additional notes)

Key accountabilities and decision ownership:

• Design and develop highly performant, scalable and stable Big Data cloud native applications
• Source data from a variety of different sources, in the correct format, meeting data quality standards and assuring timeous access to data and analytical insights.
• Build batch and real-time data pipelines, using automated testing and deployment, and enforce automated data validation and cleansing processes to ensure the accuracy, consistency, and reliability of data across all stages of the lifecycle
• Define and implement optimal data storage strategies and schema designs (e.g., using Iceberg or Hive) to ensure data is organized for high-performance querying and long term scalability
• Integrate applications with business systems to enable value from analytic models and enable decision making
• Define and implement best practices relating to cloud economics, software engineering and data engineering to ensure a well architected cloud framework with data management practices built in to each design.
• Work with the architecture team to evolve the Big Data capabilities (reusable assets/patterns) and components to support the business requirements/objectives
• Research, investigate and evaluate new technologies and methods to improve delivery and sustainability of data applications and services
• Make contributions to the process of defining best practice for the agile development of applications to run on the Big Data Platform

Core competencies, knowledge, and experience

• Building robust ETL/ELT pipelines and processing frameworks using Spark, Flink, and Airflow to handle high volume, real-time, and batch data streams.
• Data warehousing concepts, schema design (Star/Snowflake), and modern table formats like Apache Iceberg to ensure data is structured for optimal query performance and storage efficiency.
• Programming skills in Python, Scala, or Java, with a focus on writing clean, maintainable, and efficient code for data processing and automation.
• Hands on experience with distributed systems (Hadoop, Hive, Trino) and the ability to integrate diverse data sources including APIs, RDBMS, and streaming platforms like Kafka into a unified data platform.
• Advanced SQL proficiency with distributed SQL engines (e.g., Trino) for complex data modeling and performance tuning.
• Implementing automated data validation, lineage tracking, and security controls (e.g., Apache Ranger) to ensure data accuracy, privacy, and compliance with organizational standards.
• Translate complex business requirements into technical data solutions, collaborating closely with Data Scientists and Business Analysts to deliver impactful, data-driven insights.
• Strong problem solving, documentation, and cross-functional communication skills, enabling effective collaboration with platform teams, and security team and other key stakeholders.

• Design and develop highly performant, scalable and stable Big Data cloud native applications
• Source data from a variety of different sources, in the correct format, meeting data quality standards and assuring timeous access to data and analytical insights.
• Build batch and real-time data pipelines, using automated testing and deployment, and enforce automated data validation and cleansing processes to ensure the accuracy, consistency, and reliability of data across all stages of the lifecycle
• Define and implement optimal data storage strategies and schema designs (e.g., using Iceberg or Hive) to ensure data is organized for high-performance querying and long term scalability
• Integrate applications with business systems to enable value from analytic models and enable decision making
• Define and implement best practices relating to cloud economics, software engineering and data engineering to ensure a well architected cloud framework with data management practices built in to each design.
• Work with the architecture team to evolve the Big Data capabilities (reusable assets/patterns) and components to support the business requirements/objectives
• Research, investigate and evaluate new technologies and methods to improve delivery and sustainability of data applications and services
• Make contributions to the process of defining best practice for the agile development of applications to run on the Big Data Platform

• Building robust ETL/ELT pipelines and processing frameworks using Spark, Flink, and Airflow
• Data warehousing concepts, schema design (Star/Snowflake), and modern table formats like Apache Iceberg
• Programming skills in Python, Scala, or Java
• Hands on experience with distributed systems (Hadoop, Hive, Trino) and the ability to integrate diverse data sources including APIs, RDBMS, and streaming platforms like Kafka
• Advanced SQL proficiency with distributed SQL engines (e.g., Trino)
• Implementing automated data validation, lineage tracking, and security controls (e.g., Apache Ranger)
• Translate complex business requirements into technical data solutions
• Strong problem solving, documentation, and cross-functional communication skills

• Bachelor’s degree in Computer engineering, Computer Science, Information Technology, or a related field.