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Data Engineer - Big Data
Razer
, UAE
Internship
Mid
PythonSQLETL/ELTAWSSnowflakeAirflow
Free
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PythonSQLETL/ELT
About the Role
Razer is hiring a Data Engineer to design, build, and maintain scalable data processing systems for analytics and AI. The role requires 2+ years of experience in data engineering, proficiency in Python, SQL, cloud platforms, and pipeline orchestration tools.
Key Skills for This Role
PythonSQLETL/ELTAWSSnowflakeAirflow
Responsibilities
- Design, develop and maintain scalable data systems for storing, processing, and analyzing large volumes of structured and unstructured data
- Build and operate reliable data pipelines and platforms supporting batch and streaming workloads
- Manage data lakes and data warehouses, including designing data marts and implementing tokenization and vectorization for Gen AI use cases
- Leverage modern build tools and AI to enhance automation, data quality, testing, and deployment of data pipelines
- Design and implement cloud based data infrastructure in alignment with security, governance, and cost optimization policies
- Collaborate with data scientists, analysts, and business users to understand data requirements and translate them into scalable solutions
- Mentor interns for the duration of their internship to upgrade, maintain and implement data pipelines
Requirements
- Bachelor's or Master's degree in Computer Science, Engineering, or related discipline
- Minimum 2 years of hands on experience in data engineering or closely related role
- Proven experience in building and maintaining ELT/ETL pipelines using Python and SQL
- Experience with cloud based data platforms such as AWS, Snowflake, Google BigQuery, or Azure Data Lake
- Experience with pipeline orchestration tools like Airflow, distributed computing frameworks like Spark or Dask, and data transformation tools like DBT
- Proficiency with version control systems (Git) and CI/CD pipelines
- Experience with IAC tools such as Terraform, containerization tools like Docker, and container orchestration platforms like Kubernetes
Full Job Posting
Job Summary
- This role is responsible for designing, building, and maintaining scalable, high volume data processing systems that support Razer’s internal analytics and AI use cases.
- The Data Engineer enables the reliable collection, transformation, and storage of raw data into high quality, trusted datasets for data scientists, business analysts, and downstream applications.
- The role focuses on developing and operating data pipelines, data platforms, and integration frameworks to ensure data reliability, performance, and scalability.
Essential Duties And Responsibilities
- Design, develop and maintain scalable data systems that enable the organization to store, process, and analyze large volumes of structured and unstructured data used for analytics and AI use cases.
- Build and operate reliable data pipelines and platform that support batch and streaming workloads, ensure data freshness, accuracy, and completeness.
- Manage data lakes and data warehouses by populating and operationalizing them. This involves designing and creating data marts, tokenization and vectorization techniques to support Gen AI use cases.
- Leverage modern build tools and AI to enhance automation, data quality, testing, and deployment of data pipelines.
- Design and implement cloud based data infrastructure in alignment with the company’s security, governance, and cost optimization policies.
- Collaborate with data scientists, data analysts, analytics engineers, data product managers and business users to understand data requirements and translate them into scalable, maintainable data solutions.
- Mentor interns for the duration of their internship to upgrade, maintain and implement data pipelines.
Education & Experience
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related discipline.
- Minimum of 2 years of hands on experience in data engineering or a closely related role; supporting, building and improving production data pipelines and platforms.
Pre Requisites / Skills Required
- Proven experience in building and maintaining ELT/ETL pipelines using Python, SQL with software programming best practices.
- Excellent with various data processing techniques (streaming, batch, event based), managing and optimizing data storage (Data Lake, Data Warehouse, Vector Data Stores and Database, SQL, and NoSQL).
- Hands on experience with cloud based data platforms such as AWS (eg. Redshift, Sagemaker, Glue), Snowflake, Google BigQuery, or Azure Data Lake.
- Experience using pipeline orchestration tools like Airflow, distributed computing framework like Spark or Dask, data transformation tool like Data Build Tool (DBT).
- Proficiency with version control systems (Git) and CI/CD pipelines (Bitbucket pipelines) for data engineering workflows.
- Experience using IAC tools such as Terraform, containerization tools like Docker, container orchestration platforms like Kubernetes.
- Experience with BI and analytics tools like Superset, PowerBI.
- A fast learner who is interested in integrating AI into data engineering, good team player with excellent written and verbal communication skills.
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