Big Data Engineer
2025-06-20T10:07:17+00:00
Safaricom Kenya
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https://www.safaricom.co.ke/
FULL_TIME
Nairobi
kenya
00100
Kenya
Telecommunications
Computer & IT
2025-06-27T17:00:00+00:00
Kenya
8
Key Responsibilities
- Data Pipeline Development: Design, implement, and maintain robust data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data. Develop ETL (Extract, Transform, Load) processes to cleanse, enrich, and aggregate data for analysis.
- Data Storage Solutions: Architect and optimize data storage solutions, including distributed file systems, NoSQL databases, and data warehouses. Implement data partitioning, indexing, and compression techniques to maximize storage efficiency and performance.
- Big Data Technologies: Utilize and optimize big data technologies and frameworks such as Apache Hadoop, Apache Spark, Apache Flink, and Apache Kafka. Develop and maintain data processing jobs, queries, and analytics workflows using distributed computing frameworks and query languages.
- Scalability and Performance: Optimize data processing workflows for scalability, performance, and reliability. Implement parallel processing, distributed computing, and caching mechanisms to handle large-scale data processing workloads.
- Monitoring and Optimization: Develop monitoring and alerting solutions to track the health, performance, and availability of big data systems. Implement automated scaling, load balancing, and resource management mechanisms to optimize system utilization and performance.
- Data Quality and Governance: Ensure data quality and integrity throughout the data lifecycle. Implement data validation, cleansing, and enrichment processes to maintain high-quality data. Ensure compliance with data governance policies and regulatory standards.
- Collaboration and Documentation: Collaborate with cross-functional teams to understand data requirements and business objectives. Document data pipelines, system architecture, and best practices. Provide training and support to stakeholders on data engineering tools and technologies.
Qualifications
- Bachelor's or master’s degree in computer science, Engineering, or related field.
- Proven professional SQL capabilities
- Solid understanding of big data technologies, distributed systems, and database management principles.
- Proficiency in programming languages such as Python, Java, or Scala.
- Experience with big data frameworks such as Apache Hadoop, Apache Spark, or Apache Flink.
- Knowledge of database systems such as SQL databases, NoSQL databases, and distributed file systems.
- Familiarity with cloud platforms such as AWS, GCP, or Azure.
- Strong problem-solving skills and attention to detail.
- Excellent communication and collaboration skills.
- Ability to work independently and manage multiple priorities in a fast-paced environment.
Data Pipeline Development: Design, implement, and maintain robust data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data. Develop ETL (Extract, Transform, Load) processes to cleanse, enrich, and aggregate data for analysis. Data Storage Solutions: Architect and optimize data storage solutions, including distributed file systems, NoSQL databases, and data warehouses. Implement data partitioning, indexing, and compression techniques to maximize storage efficiency and performance. Big Data Technologies: Utilize and optimize big data technologies and frameworks such as Apache Hadoop, Apache Spark, Apache Flink, and Apache Kafka. Develop and maintain data processing jobs, queries, and analytics workflows using distributed computing frameworks and query languages. Scalability and Performance: Optimize data processing workflows for scalability, performance, and reliability. Implement parallel processing, distributed computing, and caching mechanisms to handle large-scale data processing workloads. Monitoring and Optimization: Develop monitoring and alerting solutions to track the health, performance, and availability of big data systems. Implement automated scaling, load balancing, and resource management mechanisms to optimize system utilization and performance. Data Quality and Governance: Ensure data quality and integrity throughout the data lifecycle. Implement data validation, cleansing, and enrichment processes to maintain high-quality data. Ensure compliance with data governance policies and regulatory standards. Collaboration and Documentation: Collaborate with cross-functional teams to understand data requirements and business objectives. Document data pipelines, system architecture, and best practices. Provide training and support to stakeholders on data engineering tools and technologies.
Bachelor's or master’s degree in computer science, Engineering, or related field. Proven professional SQL capabilities Solid understanding of big data technologies, distributed systems, and database management principles. Proficiency in programming languages such as Python, Java, or Scala. Experience with big data frameworks such as Apache Hadoop, Apache Spark, or Apache Flink. Knowledge of database systems such as SQL databases, NoSQL databases, and distributed file systems. Familiarity with cloud platforms such as AWS, GCP, or Azure. Strong problem-solving skills and attention to detail. Excellent communication and collaboration skills. Ability to work independently and manage multiple priorities in a fast-paced environment.
No Requirements
JOB-685532d57502c
Vacancy title:
Big Data Engineer
[Type: FULL_TIME, Industry: Telecommunications, Category: Computer & IT]
Jobs at:
Safaricom Kenya
Deadline of this Job:
Friday, June 27 2025
Duty Station:
Nairobi | kenya | Kenya
Summary
Date Posted: Friday, June 20 2025, Base Salary: Not Disclosed
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JOB DETAILS:
Key Responsibilities
- Data Pipeline Development: Design, implement, and maintain robust data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data. Develop ETL (Extract, Transform, Load) processes to cleanse, enrich, and aggregate data for analysis.
- Data Storage Solutions: Architect and optimize data storage solutions, including distributed file systems, NoSQL databases, and data warehouses. Implement data partitioning, indexing, and compression techniques to maximize storage efficiency and performance.
- Big Data Technologies: Utilize and optimize big data technologies and frameworks such as Apache Hadoop, Apache Spark, Apache Flink, and Apache Kafka. Develop and maintain data processing jobs, queries, and analytics workflows using distributed computing frameworks and query languages.
- Scalability and Performance: Optimize data processing workflows for scalability, performance, and reliability. Implement parallel processing, distributed computing, and caching mechanisms to handle large-scale data processing workloads.
- Monitoring and Optimization: Develop monitoring and alerting solutions to track the health, performance, and availability of big data systems. Implement automated scaling, load balancing, and resource management mechanisms to optimize system utilization and performance.
- Data Quality and Governance: Ensure data quality and integrity throughout the data lifecycle. Implement data validation, cleansing, and enrichment processes to maintain high-quality data. Ensure compliance with data governance policies and regulatory standards.
- Collaboration and Documentation: Collaborate with cross-functional teams to understand data requirements and business objectives. Document data pipelines, system architecture, and best practices. Provide training and support to stakeholders on data engineering tools and technologies.
Qualifications
- Bachelor's or master’s degree in computer science, Engineering, or related field.
- Proven professional SQL capabilities
- Solid understanding of big data technologies, distributed systems, and database management principles.
- Proficiency in programming languages such as Python, Java, or Scala.
- Experience with big data frameworks such as Apache Hadoop, Apache Spark, or Apache Flink.
- Knowledge of database systems such as SQL databases, NoSQL databases, and distributed file systems.
- Familiarity with cloud platforms such as AWS, GCP, or Azure.
- Strong problem-solving skills and attention to detail.
- Excellent communication and collaboration skills.
- Ability to work independently and manage multiple priorities in a fast-paced environment.
Work Hours: 8
Experience: No Requirements
Level of Education: bachelor degree
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