We are looking for an experienced Data Engineer with strong Python and Google Cloud Platform (GCP) expertise to design, develop, and maintain scalable data pipelines and cloud-based data solutions. The candidate will work closely with data analysts, data scientists, and business stakeholders to build reliable data platforms and enable data-driven decision-making.
Key Responsibilities
Design, develop, and maintain scalable ETL/ELT data pipelines using Python and GCP services.
Develop high-quality, reusable, and efficient Python-based data processing solutions.
Build and manage data pipelines using Google Cloud Dataflow, Cloud Composer, BigQuery, and Cloud Storage.
Design data models and optimize queries and workloads in Google BigQuery.
Develop data ingestion processes from APIs, databases, files, and other data sources.
Implement data validation, quality checks, monitoring, and error-handling mechanisms.
Optimize data pipelines for performance, scalability, reliability, and cost.
Work with stakeholders to understand data requirements and translate them into technical solutions.




Implement CI/CD processes and follow DevOps best practices for data engineering solutions.
Troubleshoot production data pipeline issues and provide root-cause analysis.
Ensure data security, governance, access control, and compliance requirements are followed.
Prepare technical documentation for data pipelines, architecture, and operational processes.
Required Skills
Strong hands-on experience with Python for data engineering and automation.
Strong experience with Google Cloud Platform (GCP).
Hands-on experience with BigQuery and SQL.
Experience with Google Cloud Storage (GCS).
Experience with Cloud Composer / Apache Airflow.
Experience with Dataflow / Apache Beam.
Strong understanding of ETL/ELT concepts and data pipeline architecture.
Valuable knowledge of relational databases and SQL optimization.
Experience working with REST APIs and different data formats such as JSON, CSV, and Parquet.
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