Design, develop, and maintain scalable ETL (Extract, Transform, Load) pipelines to process and transform raw data into usable formats for analysis and reporting.
Implement data modeling techniques to optimize data storage and retrieval, ensuring efficient data access and query performance.
Utilize data analytics tools and techniques to extract insights and patterns from large datasets, enabling data-driven decision-making within the organization.
Develop and implement automated machine learning (AutoML) solutions to streamline the process of model development and deployment.
Manage and administer relational and non-relational databases, including PostgreSQL, MySQL, MongoDB, and Google Cloud SQL, ensuring data integrity, security, and availability.
Perform database tuning and optimization to improve query performance and overall system efficiency.
Utilize cloud computing platforms such as Google Cloud Platform (GCP) to deploy and manage data processing and analytics workflows.
Leverage GCP services including Google Cloud Storage, Google Cloud Dataproc, Google Cloud Dataflow, and Google BigQuery for scalable data processing and analysis.
Coordinate and oversee the release of data engineering solutions and pipelines into production environments, ensuring smooth deployment and minimal disruption to operations.
Work in an agile development environment, following agile methodologies such as Scrum or Kanban to iteratively deliver data engineering solutions.
Participate in sprint planning, backlog grooming, and daily stand-up meetings to prioritize tasks and track progress towards project goals.