Lead Assistant Manager

We are seeking a Data Analytics Engineer with strong expertise in Python, SQL, and Google Cloud Platform (GCP) to design, develop, and maintain scalable data pipelines and analytics solutions. The ideal candidate will work closely with business stakeholders, data scientists, and engineering teams to transform raw data into actionable insights and support data-driven decision-making.

Required Skills

• Strong proficiency in Python for data processing and automation.

• Advanced SQL skills with experience in query optimization and data modeling.

• Hands-on experience with Google Cloud Platform (GCP) services such as:

• BigQuery

• Cloud Storage (GCS)

• Dataproc

• Composer/Airflow

• Experience building ETL/ELT pipelines and data integration solutions.

• Knowledge of data warehousing and analytical data models.

• Familiarity with Git and CI/CD practices.

Education

• Bachelor''s or Master''s degree in Computer Science, Data Engineering, Information Systems, or a related field.

Key Responsibilities

• Design, develop, and optimize ETL/ELT pipelines using Python and SQL.
• Build and maintain scalable data solutions on Google Cloud Platform (GCP).
• Develop and optimize data models, datasets, and reporting layers for analytics and BI requirements.
• Work with structured and semi-structured data from multiple sources.
• Ensure data quality, integrity, security, and governance across data platforms.
• Troubleshoot performance bottlenecks and optimize database queries and data pipelines.
• Collaborate with cross-functional teams to understand business requirements and deliver data solutions.
• Support data visualization and reporting initiatives using BI tools.

Key Responsibilities

• Design, develop, and optimize ETL/ELT pipelines using Python and SQL.
• Build and maintain scalable data solutions on Google Cloud Platform (GCP).
• Develop and optimize data models, datasets, and reporting layers for analytics and BI requirements.
• Work with structured and semi-structured data from multiple sources.
• Ensure data quality, integrity, security, and governance across data platforms.
• Troubleshoot performance bottlenecks and optimize database queries and data pipelines.
• Collaborate with cross-functional teams to understand business requirements and deliver data solutions.
• Support data visualization and reporting initiatives using BI tools.