60 Data Engineer jobs in Qatar

Overview

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For most tech people of our Data & Consulting division, the title of “Data engineer” or “Software engineer” does not describe everything our amazing women and men can do: data engineering, operation management, security, cloud architecture, MLOps, and more.

Presenting in every office of Artefact, working seamlessly with our consultants and data experts, our Data & Software engineers are the ones who make our data projects come true.

You will work with the team to identify your clients' needs and define innovative solutions of which you will be ownership from start to end. You manage both its conception and implementation, while also optimising the performance and scalability.

You will also coach others, keep abreast of industry news/updates and get stuck into training sessions with our business partners and suppliers, such as Microsoft, Google & Amazon. You share your knowledge, learnings and success, with the capability of presenting and communicating.

• Excellent communication & a consultative/problem-solving approach
• Strong programming skills (SQL & Python) is a MUST
• Data modelling and modern data platform design concepts
• Building and maintaining data platforms in Azure/AWS or GCP
• Experience building scalable and robust data pipelines
• Experience with dealing with data security in Azure/AWS or GCP

• Mid-Senior level

• Full-time

• Engineering and Consulting

• Advertising Services, Business Consulting and Services, and Software Development

The Data Engineer handles thedesign, development, and maintenance of data pipelines, ETL processes, anddatabase management to support AI and data science initiatives. This roleinvolves ensuring data quality, scalability, and performance across all dataengineering activities.

• Design, develop, and maintain data pipelines, ETL processes,and database systems to support AI and data science initiatives.
• Collaborate with data scientists, AI/ML engineers, and otherstakeholders to understand data requirements and ensure data availability andquality.
• Implement data governance, security, and regulatorystandards in all data engineering activities.
• Optimize data pipelines and processes for scalability,performance, and cost-efficiency.
• Monitor and ensure the performance and reliability of datasystems, identifying and resolving issues as needed.
• Stay updated with the latest advancements in dataengineering technologies and best practices.
• Provide support and guidance to other team members asneeded.
• Prepare and present data engineering reports anddocumentation to senior management and stakeholders.
• Participate in project planning and contribute to thedevelopment of project timelines and deliverables.
• Perform other duties relevant to the job as assigned by theSr. Data Engineer or senior management.
  

• Bachelor’s degree in Data Engineering, Computer Science, ora related field
• Relevant certifications (e.g., Google Cloud ProfessionalData Engineer, AWS Certified Big Data – Specialty) are preferred
• Minimum of 3 years of experience in data engineering orrelated fields
• Experience in designing and implementing data pipelines, ETLprocesses, and database systems for AI or technology-focused products
• Strong programming skills in languages such as Python, Java,or SQL
• Proficiency in data engineering tools and frameworks (e.g.,Apache Spark, Kafka)
• Excellent problem-solving and analytical skills
• Strong communication and interpersonal skills
• Attention to detail and commitment to quality
• In-depth understanding of data engineering principles, ETLprocesses, and database management
• Familiarity with cloud platforms (e.g., AWS, Azure, GoogleCloud) and their data services
• Knowledge of data governance, security, and regulatorystandards
• Ability to manage multiple tasks and prioritize effectively
• Strong attention to detail and commitment to deliveringhigh-quality work
• Ability to work independently and as part of a team
• Programming languages (e.g., Python, Java, SQL)
• Data engineering tools and frameworks (e.g., Apache Spark,Kafka)
• Data management systems (e.g., SQL, NoSQL databases)
• Collaboration and communication tools (e.g., Slack,Microsoft Teams)
  

The Principal Data Engineer handles the design, development, and maintenance of data pipelines, ETL processes, and database management to support AI and data science initiatives. This role involves ensuring data quality, scalability, and performance across all data engineering activities.

• Design, develop, and maintain data pipelines, ETL processes, and database systems to support AI and data science initiatives.
• Collaborate with data scientists, AI/ML engineers, and other stakeholders to understand data requirements and ensure data availability and quality.
• Implement data governance, security, and regulatory standards in all data engineering activities.
• Optimize data pipelines and processes for scalability, performance, and cost-efficiency.
• Monitor and ensure the performance and reliability of data systems, identifying and resolving issues as needed.
• Stay updated with the latest advancements in data engineering technologies and best practices.
• Mentor and provide guidance to junior data engineers and other team members.
• Prepare and present data engineering reports and documentation to senior management and stakeholders.
• Participate in project planning and contribute to the development of project timelines and deliverables.
• Perform other duties relevant to the job as assigned by the Head of Data & AI Engineering or senior management.

• Bachelor's degree in Data Engineering, Computer Science, or a related field.
• Relevant certifications (e.g., Google Cloud Professional Data Engineer, AWS Certified Big Data - Specialty) are preferred.
• Minimum of 8 years of experience in data engineering or related fields.
• Experience in designing and implementing data pipelines, ETL processes, and database systems for AI or technology-focused products.
• Strong programming skills in languages such as Python, SQL.
• Proficiency in data engineering tools and frameworks (e.g., Apache Spark, Kafka).
• Excellent problem-solving and analytical skills.
• Strong communication and interpersonal skills.
• Attention to detail and commitment to quality.
• In-depth understanding of data engineering principles, ETL processes, and database management.
• Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and their data services.
• Knowledge of data governance, security, and regulatory standards.
• Ability to manage multiple tasks and prioritize effectively.
• Ability to work independently and as part of a team.

The Principal Data Engineer handles the design, development, and maintenance of data pipelines, ETL processes, and database management to support AI and data science initiatives. This role involves ensuring data quality, scalability, and performance across all data engineering activities.

• Design, develop, and maintain data pipelines, ETL processes, and database systems to support AI and data science initiatives.
• Collaborate with data scientists, AI/ML engineers, and other stakeholders to understand data requirements and ensure data availability and quality.
• Implement data governance, security, and regulatory standards in all data engineering activities.
• Optimize data pipelines and processes for scalability, performance, and cost-efficiency.
• Monitor and ensure the performance and reliability of data systems, identifying and resolving issues as needed.
• Stay updated with the latest advancements in data engineering technologies and best practices.
• Mentor and provide guidance to junior data engineers and other team members.
• Prepare and present data engineering reports and documentation to senior management and stakeholders.
• Participate in project planning and contribute to the development of project timelines and deliverables.
• Perform other duties relevant to the job as assigned by the Head of Data & AI Engineering or senior management.

• Bachelor’s degree in Data Engineering, Computer Science, or a related field.
• Relevant certifications (e.g., Google Cloud Professional Data Engineer, AWS Certified Big Data – Specialty) are preferred.
• Minimum of 8 years of experience in data engineering or related fields.
• Experience in designing and implementing data pipelines, ETL processes, and database systems for AI or technology-focused products.
• Strong programming skills in languages such as Python, SQL.
• Proficiency in data engineering tools and frameworks (e.g., Apache Spark, Kafka).
• Excellent problem-solving and analytical skills.
• Strong communication and interpersonal skills.
• Attention to detail and commitment to quality.
• In-depth understanding of data engineering principles, ETL processes, and database management.
• Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and their data services.
• Knowledge of data governance, security, and regulatory standards.
• Ability to manage multiple tasks and prioritize effectively.
• Ability to work independently and as part of a team.

Experience : 4-6yrs

Job description :

We seek a talented Data Engineer with AI & ML knowledge to join our team. As a Data Engineer or MLOps Engineer, your primary responsibility will be to develop & integrate ML solutions that focus on technology improvements. Specifically, you will be working on projects involving leveraging AI/ML for Data Management Efficiencies & Query Optimizations.

Responsibilities:

1. Collaborate with cross-functional teams such as Data Scientists, Product Partners, and Partner Team Developers to identify opportunities for Big Data, Query (Spark, Hive SQL, BigQuery, SQL) tuning opportunities that can be solved using machine learning and generative AI.
2. Write clean, high-performance, high-quality, maintainable code.
3. Create backend applications using Python, Docker, Google Cloud & in-house ML frameworks to orchestrate end-to-end applications.
4. Design and develop Big Data Engineering Solutions & generative AI Applications ensuring scalability, efficiency, and maintainability of such solutions.
5. Implement prompt engineering techniques to fine-tune and enhance LLMs for better performance and application-specific needs.
6. Stay abreast of the latest advancements in the field of Generative AI Application Development and actively contribute to the research and development of new Generative AI Applications.

Requirements:

1. Proven experience working as a Big Data & MLOps Engineer, with a focus on Python, Google Cloud, Spark, Spark SQL, BigQuery, and Generative AI Applications.
2. Deep understanding and experience in tuning Dataproc, BigQuery, and Spark Applications.
3. Solid knowledge of software engineering best practices, including version control systems (e.g., Git), code reviews, and testing methodologies.

The Sr. Data Engineer supports the design, development, and maintenance of data pipelines, ETL processes, and database systems to support AI and data science initiatives. This role involves ensuring data quality, scalability, and performance across all data engineering activities.

1. Support the design, development, and maintenance of data pipelines, ETL processes, and database systems to support AI and data science initiatives.
2. Collaborate with data scientists, AI/ML engineers, and other stakeholders to understand data requirements and ensure data availability and quality.
3. Implement data governance, security, and regulatory standards in all data engineering activities.
4. Optimize data pipelines and processes for scalability, performance, and cost-efficiency.
5. Monitor and ensure the performance and reliability of data systems, identifying and resolving issues as needed.
6. Stay updated with the latest advancements in data engineering technologies and best practices.
7. Mentor and provide guidance to junior data engineers and other team members.
8. Prepare and present data engineering reports and documentation to senior management and stakeholders.
9. Participate in project planning and contribute to the development of project timelines and deliverables.
10. Perform other duties relevant to the job as assigned by the Principal Data Engineer or senior management.

• Bachelor’s degree in Data Engineering, Computer Science, or a related field.
• Relevant certifications (e.g., Google Cloud Professional Data Engineer, AWS Certified Big Data – Specialty) are preferred.
• Minimum of 5 years of experience in data engineering or related fields.
• Experience in designing and implementing data pipelines, ETL processes, and database systems for AI or technology-focused products.
• Strong programming skills in languages such as Python, SQL.
• Proficiency in data engineering tools and frameworks (e.g., Apache Spark, Kafka).
• Excellent problem-solving and analytical skills.
• Strong communication and interpersonal skills.
• Attention to detail and commitment to quality.
• In-depth understanding of data engineering principles, ETL processes, and database management.
• Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and their data services.
• Knowledge of data governance, security, and regulatory standards.
• Ability to manage multiple tasks and prioritize effectively.
• Ability to work independently and as part of a team.