Data Engineer at Tezza Business Solutions Ltd

Job Description 

We are seeking a talented and experienced Data Engineer to join our team.

• The Data Engineer will be responsible for designing, building, and maintaining scalable data pipelines and infrastructure to support our data analytics and business intelligence needs.
• The ideal candidate will have a strong background in data engineering, a deep understanding of data architecture, and experience with big data technologies.

Key Responsibilities

• Data Pipeline Development: Design, develop, and maintain scalable data pipelines to process and analyze large datasets from various sources.
• Data Integration: Integrate data from multiple data sources and ensure data quality, consistency, and reliability.
• Data Warehousing: Build and maintain data warehouses and data lakes to support analytics and reporting needs.
• Data Transformation: Implement data transformation and cleaning processes to ensure data is ready for analysis.
• Performance Optimization: Optimize data processing performance and troubleshoot any issues related to data pipelines and infrastructure.
• Collaboration: Work closely with data scientists, analysts, and other stakeholders to understand their data needs and provide the necessary data infrastructure.
• Documentation: Document data engineering processes, data flows, and infrastructure designs.
• Security and Compliance: Ensure data security, privacy, and compliance with relevant regulations and standards.
• Continuous Improvement: Stay up-to-date with the latest industry trends and technologies, and continuously improve data engineering practices and processes.

Qualifications

• Education: Bachelor’s degree in Computer Science, Information Technology, or a related field. Master’s degree is a plus.
• Experience: Minimum of 3-5 years of experience in data engineering or a similar role.
• Technical Skills: Proficiency in programming languages such as Python, Java, or Scala. Strong SQL skills.
• Big Data Technologies: Experience with big data technologies such as Hadoop, Spark, Kafka, and NoSQL databases.
• Data Warehousing: Experience with data warehousing solutions such as Amazon Redshift, Google BigQuery, or Snowflake.
• ETL Tools: Familiarity with ETL tools and processes.
• Cloud Platforms: Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Analytical Skills: Strong analytical and problem-solving skills.
• Communication Skills: Excellent verbal and written communication skills.
• Collaboration: Ability to work effectively in a collaborative team environment.
• Certification: Relevant certifications (e.g., AWS Certified Data Analytics, Google Professional Data Engineer) are a plus.