Python Data Engineer

Role Overview: You will be responsible for designing and deploying robust ELT/ETL pipelines using Python and SQL to ingest data from internal applications, third-party APIs, and production databases. Additionally, you will architect, write, and maintain modular dbt models to transform raw data into structured analytics layers. Leveraging the AWS ecosystem, you will store, process, and orchestrate large-scale datasets efficiently and securely. Monitoring and tuning complex SQL queries, dbt runs, and warehouse configurations will be crucial to keep compute costs down and pipeline speeds high. Implementing automated testing, documentation, and data validation rules to ensure data quality and collaborating with cross-functional teams to understand data requirements will also be part of your responsibilities. Key Responsibilities: - Design, build, and maintain scalable, robust ELT/ETL pipelines using Python and SQL. - Architect and implement clean data models within the cloud data warehouse using dbt to ensure high performance, data quality, and documentation. - Leverage AWS services for storage, processing, and orchestration of large-scale datasets efficiently. - Optimize complex SQL queries, dbt models, and data pipelines to minimize latency and cloud compute costs. - Implement automated testing to ensure data integrity, consistency, and reliability. - Collaborate with Data Analysts, Data Scientists, and product teams to understand data needs and deliver production-ready data structures. Qualification Required: - 4+ years of professional data engineering experience in a production environment. - Mastery of advanced SQL and a deep understanding of data warehousing principles. - Proficiency in writing clean, reusable Python code for data extraction, manipulation, and pipeline orchestration. - Hands-on experience using dbt for managing data transformations, testing frameworks, and documentation. - Practical experience with key AWS services such as S3, Redshift, Lambda, and EC2. - Familiarity with data workflow tools like Apache Airflow, Prefect, or Dagster and robust version control using Git. GlobalLogic, a Hitachi Group Company, is a trusted digital engineering partner known for collaborating with the world's largest companies to create innovative digital products and experiences. Since 2000, GlobalLogic has been at the forefront of the digital revolution, transforming businesses and industries through intelligent products, platforms, and services. The company offers exciting projects in industries like High-Tech, communication, media, healthcare, retail, and telecom, providing employees with a collaborative environment, work-life balance, professional development opportunities, excellent benefits, and fun perks such as sports events, cultural activities, and corporate parties. Role Overview: You will be responsible for designing and deploying robust ELT/ETL pipelines using Python and SQL to ingest data from internal applications, third-party APIs, and production databases. Additionally, you will architect, write, and maintain modular dbt models to transform raw data into structured analytics layers. Leveraging the AWS ecosystem, you will store, process, and orchestrate large-scale datasets efficiently and securely. Monitoring and tuning complex SQL queries, dbt runs, and warehouse configurations will be crucial to keep compute costs down and pipeline speeds high. Implementing automated testing, documentation, and data validation rules to ensure data quality and collaborating with cross-functional teams to understand data requirements will also be part of your responsibilities. Key Responsibilities: - Design, build, and maintain scalable, robust ELT/ETL pipelines using Python and SQL. - Architect and implement clean data models within the cloud data warehouse using dbt to ensure high performance, data quality, and documentation. - Leverage AWS services for storage, processing, and orchestration of large-scale datasets efficiently. - Optimize complex SQL queries, dbt models, and data pipelines to minimize latency and cloud compute costs. - Implement automated testing to ensure data integrity, consistency, and reliability. - Collaborate with Data Analysts, Data Scientists, and product teams to understand data needs and deliver production-ready data structures. Qualification Required: - 4+ years of professional data engineering experience in a production environment. - Mastery of advanced SQL and a deep understanding of data warehousing principles. - Proficiency in writing clean, reusable Python code for data extraction, manipulation, and pipeline orchestration. - Hands-on experience using dbt for managing data transformations, testing frameworks, and documentation. - Practical experience with key AWS services such as S3, Redshift, Lambda, and EC2. - Familiarity with data workflow tools like Apache Airflow, Prefect, or Dagster and robust version control using Git. GlobalLogic, a Hitachi Group Company, is a t