Data Engineer

Description

Job Summary: As a Data Engineer, you will be responsible for implementing and optimizing cloud-based data architectures, ensuring data quality, security, and governance, while collaborating closely with Engineering and Analytics teams. Key Highlights: 1. Cloud data architecture optimization (GCP and Azure) 2. Data quality, security, and governance assurance 3. Data pipeline development (ETL/ELT) and process automation Responsibilities and Duties Implement and optimize cloud data architectures (GCP and Azure); Ensure data quality, security, and governance by adhering to best practices and compliance policies; Collaborate with Engineering and Analytics teams to understand data requirements and propose scalable solutions; Monitor and optimize data system performance, identifying bottlenecks and improvement opportunities; Automate data ingestion, transformation, and delivery processes to improve efficiency and scalability; Manage data lakes and data warehouses; Implement observability solutions to ensure data platform reliability. Requirements and Qualifications Proven experience developing data pipelines (ETL/ELT) using tools such as Apache Airflow, Databricks, or similar; Proficiency in programming languages such as Python; Experience with relational and non-relational databases (SQL, NoSQL); Knowledge of data lakes and data warehouses (e.g., BigQuery); Experience with code versioning tools (Git) and CI/CD; Familiarity with data governance, security, and sound data modeling practices; Ability to work effectively in agile and collaborative environments. Preferred Qualifications Experience with event-driven architecture (Kafka); Familiarity with observability tools; Experience with distributed processing platforms (Spark); **Requirements:** Data Engineer Responsibilities and Duties Implement and optimize cloud data architectures (GCP and Azure); Ensure data quality, security, and governance by adhering to best practices and compliance policies; Collaborate with Engineering and Analytics teams to understand data requirements and propose scalable solutions; Monitor and optimize data system performance, identifying bottlenecks and improvement opportunities; Automate data ingestion, transformation, and delivery processes to improve efficiency and scalability; Manage data lakes and data warehouses; Implement observability solutions to ensure data platform reliability. Requirements and Qualifications Proven experience developing data pipelines (ETL/ELT) using tools such as Apache Airflow, Databricks, or similar; Proficiency in programming languages such as Python; Experience with relational and non-relational databases (SQL, NoSQL); Knowledge of data lakes and data warehouses (e.g., BigQuery); Experience with code versioning tools (Git) and CI/CD; Familiarity with data governance, security, and sound data modeling practices; Ability to work effectively in agile and collaborative environments. Preferred Qualifications Experience with event-driven architecture (Kafka); Familiarity with observability tools; Experience with distributed processing platforms (Spark);