Data Engineer

KUN Sports; Jeddah; On-site; SAR 150,000 - 200,000; Full time

Job summary

A leading sports organization in Saudi Arabia is seeking a Data Engineer responsible for designing and maintaining scalable data pipelines and data architectures. This role involves developing ETL/ELT processes, ensuring data quality and compliance, and supporting analytics/reporting capabilities across the organization. The ideal candidate will have a Bachelor’s Degree in Computer Science and 4–6 years of relevant experience, along with strong SQL and Python proficiency, and hands-on experience with ETL tools and cloud data environments.

Qualifications

• 4–6 years of experience in data engineering or data warehousing.
• Experience with relational and NoSQL databases.
• Hands-on experience with data orchestration tools.

Responsibilities

• Design and maintain scalable ETL/ELT pipelines.
• Optimize pipeline performance and monitor data accuracy.
• Develop and maintain robust system integrations and APIs.
• Enable BI teams with datasets for reporting.

Skills

SQL proficiency

Python

ETL tools

Data orchestration

Data modeling

Troubleshooting skills

Education

Bachelor’s Degree in Computer Science or related field

Tools

PostgreSQL

MySQL

MongoDB

Airflow

AWS

Azure

GCP

Power BI

Tableau

Looker

Job description

Role purpose

The Data Engineer is responsible for designing, building, and maintaining scalable data pipelines, integrations, and data warehouse structures that support organizational analytics and reporting. The role ensures high-quality, reliable, and secure data flow across systems, enabling business units to make data-driven decisions aligned with Kun Sports’ strategic objectives.

Key Accountabilities & Activities

Data Pipeline Development & Management

• Design, build, and maintain scalable ETL/ELT pipelines for ingestion, cleaning, transformation, and loading of structured and unstructured data.
• Build workflows using Python frameworks such as FastAPI, Redis, RabbitMQ, etc.
• Automate and orchestrate data workflows using Airflow, DBT, Prefect, or cloud-native schedulers.
• Implement automated job monitoring, error logging, and alerting mechanisms.
• Optimize pipeline performance to reduce latency, processing time, and resource consumption.
• Develop real-time streaming pipelines using Kafka, Kinesis, or similar technologies.

Data Warehouse & Database Architecture

• Build and maintain enterprise data warehouse schemas (star, snowflake, normalized).
• Model data structures to support dashboards, analytics, and predictive models.
• Set up database indexing, partitioning, and clustering to enhance query performance.
• Maintain metadata repositories, data catalogs, and lineage tracking.

Data Quality, Governance & Compliance

• Establish validation rules, anomaly detection, and data quality scoring frameworks.
• Ensure compliance with internal data governance, security policies, and data retention rules.
• Implement RBAC (role-based access control) and least-privilege access policies for all environments.
• Monitor and maintain data accuracy, completeness, timeliness, and integrity.
• Document data sources, transformation logic, and quality standards.

System Integration & API Development

• Develop and maintain robust integrations with HRMS (Menaitech), CRM, ERP, POS, Sentinel, fitness systems, and external vendors.
• Build and maintain RESTful APIs and data endpoints for cross-department usage.
• Troubleshoot API failures, authentication issues, and versioning conflicts.
• Create scalable integration architectures for future system expansion.

Analytics, Reporting & Business Support

• Enable BI team with optimized datasets for Power BI, Tableau, Looker, or internal dashboards.
• Support advanced analytics, machine learning pipelines, and predictive modeling.
• Translate business and department requirements into data architecture solutions.
• Build reusable data models for Finance, HR, Operations, Sales, and Marketing.
• Provide deep-dive analysis and root cause investigations for data inconsistencies.

Cloud Environment & Infrastructure

• Manage cloud storage, compute instances, networking, and access permissions (AWS/Azure/GCP).
• Set up CI/CD pipelines for data engineering deployments.
• Manage Terraform / IaC for provisioning infrastructure.
• Ensure cost optimization for cloud-based workloads and data storage.

Maintenance, Documentation & Continuous Improvement

• Maintain detailed technical documentation for data pipelines, schemas, integration flows, APIs, and architecture.
• Conduct regular system maintenance, patch updates, and optimization.
• Create runbooks for troubleshooting common issues and recovery procedures.
• Conduct capacity planning and scalability assessments.
• Recommend and implement new tools, technologies, and best practices.

Requirements for Role

Experience & qualifications

• Bachelor’s Degree in Computer Science, Information Systems, Data Engineering, or related field.
• 4–6 years of experience in data engineering, data warehousing, or backend data infrastructure.

Knowledge & skills

• Strong SQL proficiency and experience with relational and NoSQL databases (e.g., PostgreSQL, MySQL, MongoDB).
• Hands‑on experience with ETL tools, data orchestration (Airflow, AWS Glue, DBT, etc.).
• Experience with cloud data environments (AWS, Azure, or GCP).
• Strong proficiency in Python.
• Solid understanding of APIs, data modeling, and data architecture principles.
• Experience with BI tools connectivity (Power BI, Tableau, Looker).
• Strong troubleshooting, debugging, and problem‑solving skills.
• Strong documentation, communication, and stakeholder management skills.