Overview
The Economics, Data and Statistics Sector is tasked to build a brand-new Statistics Office for NEOM in a greenfield environment in order to produce robust data and statistics about people, the economy and different sectors of interest. Building such a Statistics Office in a fully digitalized context offers unique opportunities to gain deep real-time statistical insights and take the production of official statistics to a level way beyond what is possible in legacy environments. We are seeking a highly skilled and experienced Manager for Data Engineering and Solutions to lead the design, development, and optimization of our data infrastructure and software solutions.
Key Accountabilities & Activities
- ETL and Data Pipelines
- Lead the design and implementation of data processing pipelines, ETL (Extract, Transform, Load) workflows, and data integration solutions to support the acquisition, storage, and transformation of large volumes of structured and unstructured data.
- Develop and maintain scalable and high-performance databases, data warehouses, and data lakes using modern technologies such as SQL, NoSQL, Hadoop, Spark, and others.
- Collaborate with cross-functional teams to gather requirements, define technical specifications, and architect data solutions that meet business needs and performance objectives.
- Design and build data APIs, microservices, and web applications to enable real-time data collection, visualization, and analysis for internal and external stakeholders.
- Implement best practices for data governance, security, and compliance, ensuring data integrity, confidentiality, and regulatory compliance throughout the data lifecycle.
- Optimize data infrastructure and software systems for efficiency, reliability, and scalability, and troubleshoot performance issues to ensure uninterrupted data operations.
- Stay abreast of emerging technologies, tools, and trends in data engineering, software development, and cloud computing, and evaluate their potential for adoption in our environment.
- Develop and maintain data documentation, including data dictionaries, metadata, and data lineage documentation, to ensure transparency and facilitate knowledge sharing across teams.
- Lead the evaluation and implementation of data quality assessment frameworks and tools to monitor data quality, identify anomalies, and ensure data accuracy and reliability.
- Implement strategies for data lifecycle management, including data retention policies, archival procedures, and data disposal protocols, to ensure compliance with regulatory requirements and optimize data storage resources.
Background, Skills & Qualifications
- Minimum of 7 years of professional experience in data engineering, software development, or a related role, with a strong background in building and maintaining data infrastructure and software systems.
- Proficiency in programming languages such as Python, Java, Scala, or similar languages, and experience with software development frameworks and tools.
- Strong knowledge of database systems, data modeling, and SQL query optimization, with experience working with relational databases and NoSQL databases.
- Hands-on experience with big data technologies such as Hadoop, Spark, Kafka, and distributed computing platforms.
- Familiarity with cloud computing platforms and containerization technologies for deploying and managing data applications.
- Excellent problem-solving skills, with the ability to analyze complex data issues, debug system failures, and implement effective solutions.
- Strong communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams and communicate technical concepts to non-technical stakeholders.
- Bachelor's or Master's degree or equivalent in computer science, or a related field with a focus on data engineering, software development, or database management.
