Description:

Design and implement scalable data pipelines, ETL processes, and data integration workflows using Google Cloud Platform (GCP) tools and services.
Build and maintain data warehouses and data lakes on GCP, ensuring data integrity, accuracy, and security.
Work closely with data scientists, analysts, and stakeholders to understand data needs and deliver actionable insights.
Optimize data pipelines and storage solutions for performance, cost, and reliability.
Automate data processing tasks and workflows to improve efficiency and reduce manual effort.
Monitor data systems, troubleshoot issues, and implement solutions to maintain system health and performance.
Document data architectures, processes, and best practices to ensure knowledge sharing and consistency.

Requirements:

Minimum of 3 years of experience in data engineering or a similar role, Python programming, and SQL.
Strong understanding of data modeling, design, ETL processes, and data warehousing concepts.
Experience with data visualization tools (e.g., Tableau, Power BI, Looker Studio) and proficiency in dashboard development and reporting.
Experience in Google Cloud Platform (GCP) stack including Google Cloud Storage, BigQuery, Compute Engine, Cloud Composer, Cloud Function, DataFlow, Pub/Sub.
Familiarity with Kubernetes, Apache Airflow, and DataOps.
Experience in developing, deploying, and maintaining end-to-end data pipelines.
Comfortable with using IDE, git, or other software versioning systems.
Experience with Agile methodologies.
Nice to have: Familiarity with machine learning and AI concepts, stakeholder management, Google Cloud Professional Data Engineer certification, Databricks, ML Ops, ML Flow, Docker.