Data Engineer Specialist

Saudi Pro League (SPL); Riyadh; On-site; SAR 120,000 - 180,000; Full time

Job summary

A leading company in the sports sector is seeking a Data Engineer to design and maintain data infrastructure and pipelines. This role involves collaborating with IT and business teams to ensure data quality and support analytics. The ideal candidate will have extensive experience in ETL development and cloud platforms, contributing to data-driven decision-making across the organization.

Qualifications

• 5+ years of experience in Data Engineering or related fields.
• Proven expertise in building ETL pipelines and cloud-based data platforms.
• Experience with structured, semi-structured, and unstructured data.

Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines.
• Ensure data quality and optimize for reporting and analytics.
• Integrate data from multiple systems into a centralized repository.

Skills

Data Quality Management

ETL Development

Data Security

Data Integration

Cloud Platforms

Python

SQL

Apache Airflow

Education

Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems

Tools

AWS

Azure

GCP

PostgreSQL

MongoDB

Snowflake

Power BI

Tableau

Job description

Bachelors in Computer Application(Computers)

Nationality

Any Nationality

Vacancy

1 Vacancy

Job Description

Role Main Purpose:

The Data Engineer is responsible for designing, developing, and maintaining data infrastructure, pipelines, and integrations that support enterprise-wide data analytics, reporting, and business intelligence. The role focuses on ensuring data availability, quality, and security to enable effective decision-making across the organization. The Data Engineer will work closely with IT, data analysts, business intelligence teams, and corporate stakeholders to build scalable, automated, and cloud-optimized data solutions that integrate with various business applications, ERP systems, CRM platforms, and data warehouses.

Responsibilities:

• Design, develop, and maintain scalable ETL/ELT pipelines for data extraction, transformation, and loading from multiple internal and external sources.
• Ensure data is clean, normalized, and optimized for reporting and analytics.
• Automate data workflows and batch processes to improve operational efficiency.
• Use Python, SQL, Apache Airflow to manage data transformations.
• Gathering, wrangling, and verifying data from various sources.
• Design, develop, and maintain scalable, real-time data pipelines for data ingestion, transformation, and storage.
• Ensure seamless data integration from various sources
• Implement big data frameworks and cloud-based architecture (e.g., AWS, Azure, GCP) tailored for sports analytics
• Database Management & Optimization
  
  • Manage structured and unstructured data across relational (SQL) and NoSQL databases (e.g., PostgreSQL, MongoDB, Snowflake).
  • Optimize database performance to support large-scale data analytics.
  • Implement data partitioning, indexing, and caching strategies to enhance query performance.
• Business Intelligence & Reporting Support
  
  • Enable data-driven decision-making by supporting BI teams with data models and analytical datasets.
  • Work with Power BI, Tableau, or other visualization tools to optimize reporting performance.
  • Implement self-service analytics frameworks to empower business users with easy access to data
• Data Integration & Business Application Connectivity
  
  • Integrate data from multiple systems to create a centralized data repository.
  • Develop and manage APIs and middleware solutions for seamless data flow between corporate applications.
  • Work with third-party data providers and external APIs to enrich business data.
• Assist in developing predictive analytics, forecasting models, and anomaly detection use cases.
• Ensure efficient feature engineering and data preparation for machine learning initiatives.
• Data Security & Compliance
  
  • Security Measures: work with the Data Protection and Cybersecurity to Implement the data protection and cybersecurity measures to protect the application stack from threats.
  • Access Control: Managing who can access the application and what they can do.
  • Conduct regular data integrity checks and audits to maintain accuracy and reliability.
• Data Error Checking: Regularly verifying data to identify and correct errors.
• Validation Procedures: Ensuring data meets specific criteria before it's used.
• Application Integration
  
  • Integrate sports data platforms with ERP, CRM, ticketing, and broadcasting systems to ensure seamless data flow.
  • Maintain API lifecycle management for data-sharing across internal and external stakeholders.
• Data Lifecycle Management & Archiving
  
  • Data Migration: Ensuring data is securely transferred to new systems.
  • Assess Impact: Evaluate the impact of retiring these applications on business operations and data integrity.
  • Data Extraction and Transformation: Thoroughly examine and extract all necessary data from the application.
  • Data Archiving: Coherent and accessible data archive, ensuring it includes necessary metadata for future reference.
  • Maintain Data: Establish retention policies to manage the archived data according to business and regulatory requirements.

Best Practice:

• Standardization: Implement best practices for data modelling, metadata management, and data architecture.
• Automation: Use CI/CD pipelines for data engineering workflows to enhance agility.
• Data Quality Management: Enforce data validation, anomaly detection, and reconciliation processes.
• Security & Compliance: Regularly audit and ensure compliance with corporate data governance policies.
• Collaboration: Work closely with business, IT, and analytics teams to align data solutions with enterprise needs.

Education & Experience:

• 5+ years of experience in Data Engineering, Data Architecture, or Big Data Processing.
• Proven expertise in building ETL pipelines, data warehouse management, and cloud-based data platforms.
• Experience working with structured, semi-structured, and unstructured data.
• Bachelor s or Master s degree in Computer Science, Data Engineering, Information Systems, or a related field.
• Certifications in AWS Data Engineering, Azure Data Fundamentals, or Google Data Engineer (Preferred).

Knowledge & Skills:

• Ability to build relationships based on transparency
• Adopt, Apply and promote the SPL culture and values (Performance - Ambition - Governance Professionalism)
• Ability to transfer confidence to team members
• Ability to transfer knowledge and experience to team members

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