Data Engineer (EMIRATI)

Salt; Abu Dhabi; Presencial; AED 180.000 - 360.000; Jornada completa

Descripción de la vacante

Salt in Abu Dhabi is seeking a hands-on Data Engineer for a technology and data opportunity. You will work behind the scenes to make data dependable, accessible, and ready for use, helping move data across systems while supporting quality and reliability.

Key responsibilities include monitoring integrations to keep data flows reliable, onboarding new data sources, validating data quality, designing practical data models, and building automated ETL/ELT pipelines with SQL, Python, APIs, and secure

Formación

• 3+ years in data engineering or related field.
• Hands-on with SQL and Python; experience with data pipelines.
• Experience with ETL/ELT concepts, APIs and data integration.
• Knowledge of data models, validation, quality controls, and troubleshooting.
• Bachelor's degree in CS/IS/Data Engineering or related field.

Responsabilidades

• Monitor integrations to keep data flows reliable and timely.
• Bring new data sources into the environment via stable integration.
• Improve data quality through validation, testing, reconciliation.
• Design and maintain practical data models for evolving analytics needs.
• Develop automated data pipelines using ETL/ELT, SQL, Python, APIs.
• Support deployment processes and maintain technical documentation.

Conocimientos

SQL

Python

Data pipelines

ETL/ELT

APIs

Data quality

Testing

Deployment

Networking basics

Security

Educación

Bachelor's degree in CS/IS/Data Eng

Descripción del empleo

We are hiring a 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 for a technology and data opportunity in 𝐀𝐛𝐮 𝐃𝐡𝐚𝐛𝐢.

This role is ideal for a hands-on engineer who enjoys working behind the scenes to make data dependable, accessible, and ready for use. You will help strengthen the movement of data across different systems while supporting the quality and reliability of the wider data environment.

Key Responsibilities

• Keep critical data flows reliable by monitoring integrations, investigating interruptions, and resolving issues affecting the availability, freshness, or accuracy of data.
• Work with technical and business teams to bring additional data sources into the environment, translating source structures and requirements into stable integration solutions.
• Strengthen the quality of incoming data through validation, testing, reconciliation, and analysis before information is made available for downstream use.
• Contribute to the design and maintenance of practical data models that support evolving analytical and operational requirements.
• Develop and enhance automated data pipelines using appropriate ETL/ELT methods, SQL, Python, APIs, database connectivity, and secure transfer mechanisms.
• Support a well-controlled engineering environment by improving deployment processes, maintaining technical documentation, and applying appropriate data security and access practices.

Requirements

• Hands-on proficiency with SQL and Python, together with practical experience developing or supporting data pipelines and integration workflows.
• 3+ years of experience in data engineering, data integration, backend data processing, or a related technical discipline.
• Good understanding of ETL/ELT concepts, APIs, database interfaces, secure file exchange, and connecting information across different source systems.
• Experience working with data models, validation rules, quality controls, and troubleshooting data issues across interconnected environments.
• Familiarity with automated testing and deployment practices, as well as the basic networking and security considerations involved in system-to-system data exchange.
• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field; broader exposure to analytics, big data, or complex multi-source environments would be beneficial.

What Makes This Role Strong

This opportunity gives you exposure to end-to-end data engineering challenges, from bringing new sources into an established environment to improving pipeline reliability, data quality, and the technical foundations that support analytics.