Design and implement data-centric solutions on Google Cloud Platform (GCP) using various GCP tools like Big Query, Google Cloud Storage, Cloud SQL, Memory Store, Dataflow, Dataproc, Artifact Registry, Cloud Build, Cloud Run, Vertex AI, Pub/Sub, GCP APIs.
Build ETL pipelines to ingest the data from heterogeneous sources into our system.
Develop data processing pipelines using programming languages like Java and Python to extract, transform, and load (ETL) data.
Create and maintain data models, ensuring efficient storage, retrieval, and analysis of large datasets.
Deploy and manage databases, both SQL and NoSQL, such as Bigtable, Firestore, or Cloud SQL, based on project requirements.
Optimize data workflows for performance, reliability, and cost-effectiveness on the GCP infrastructure.
Implement version control and CI/CD practices for data engineering workflows to ensure reliable and efficient deployments.
Utilize GCP monitoring and logging tools to proactively identify and address performance bottlenecks and system failures.
Troubleshoot and resolve issues related to data processing, storage, and retrieval.
Promptly address code quality issues using SonarQube, Checkmarx, Fossa, and Cycode throughout the development lifecycle.
Implement security measures and data governance policies to ensure the integrity and confidentiality of data.
Collaborate with stakeholders to gather and define data requirements, ensuring alignment with business objectives.
Develop and maintain documentation for data engineering processes, ensuring knowledge transfer and ease of system maintenance.
Participate in on-call rotations to address critical issues and ensure the reliability of data engineering systems.
Provide mentorship and guidance to junior team members, fostering a collaborative and knowledge-sharing environment.