Identify and implement process improvements, automating tasks, and enhancing data system efficiency.
Develop new features and optimize existing ones across the data ecosystem to improve scalability and performance.
Assemble complex data sets to support functional and analytical needs.
Design and maintain efficient ETL processes, focusing on data consistency and quality.
Create automated monitoring solutions and support infrastructure for analytics.
Oversee data quality and governance, maintaining secure data environments and robust infrastructure (including AWS and database systems).
Document data systems, providing clear guidance to internal users.
Maintain best practices across databases, focusing on security, performance, and data integrity.
Tech Stack:
Python, SQL, PySpark
AWS, Airflow, Docker, Terraform
Requirements:
Degree in Computer Science, Information Systems, Engineering, or related field, or equivalent professional experience.
At least 5 years of experience in data engineering, including 2 years working with large-scale data pipelines.
Expertise in Python and SQL, with knowledge of PostgreSQL and MS SQL.
Strong experience with cloud services such as AWS, Azure, or GCP.
Proficiency in version control, CI/CD, and tools like GitHub.
Knowledge of Glue, PySpark, and other big data tools is a plus.
Strong problem-solving skills, with the ability to manage data lifecycle and process optimization.
Effective communication and teamwork skills.
How to Apply: Send your detailed CV to OR apply online.Only shortlisted candidates will be contacted. If you have not been contacted within 2 weeks, your application has been declined. If you are an innovative data engineer with a knack for building efficient data systems, apply now to make a difference with our client.
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