Data Engineer - ETL/ELT Pipeline

Role Overview: As a Data Engineer with over 5 years of experience in data engineering or related roles, you will be responsible for developing ETL/ELT pipelines and working with cloud data platforms, specifically GCP. Your technical expertise will include proficiency in programming languages such as Python, SQL, and preferably Golang. You will also have experience with a variety of databases including PostgreSQL, MySQL, and Redis, as well as big data tools like Apache Spark, Apache Kafka, and Apache Airflow. Additionally, you will work with various cloud platforms such as GCP, data warehousing tools like Google BigQuery, and data visualization tools like Superset, Looker, Metabase, and Tableau. Key Responsibilities: - Develop and maintain ETL/ELT pipelines for efficient data processing - Utilize cloud data platforms, specifically GCP, for data storage and processing - Work with a variety of programming languages, databases, big data tools, cloud platforms, data warehousing solutions, and data visualization tools - Collaborate with cross-functional teams to ensure data quality and integrity - Stay updated on the latest technologies and tools in data engineering and implement best practices Qualifications Required: - Minimum 5 years of experience in data engineering or related roles - Proven experience with ETL/ELT pipeline development - Proficiency in Python, SQL, and preferably Golang - Experience with cloud data platforms, especially GCP - Familiarity with databases such as PostgreSQL, MySQL, and Redis - Knowledge of big data tools like Apache Spark, Apache Kafka, and Apache Airflow - Strong problem-solving and analytical skills - Excellent communication and collaboration abilities - Ability to work independently and in team environments - Attention to detail and focus on data quality - Continuous learning mindset Additional Details: The company emphasizes the importance of working with machine learning pipelines, MLOps, data governance, and data catalog tools. Familiarity with business intelligence tools like Tableau, Power BI, and Looker is preferred. Experience using AI-powered tools for coding acceleration, task automation, and system design assistance is valued at the company. The company believes in leveraging machine capabilities to enhance productivity and efficiency in data engineering processes. Role Overview: As a Data Engineer with over 5 years of experience in data engineering or related roles, you will be responsible for developing ETL/ELT pipelines and working with cloud data platforms, specifically GCP. Your technical expertise will include proficiency in programming languages such as Python, SQL, and preferably Golang. You will also have experience with a variety of databases including PostgreSQL, MySQL, and Redis, as well as big data tools like Apache Spark, Apache Kafka, and Apache Airflow. Additionally, you will work with various cloud platforms such as GCP, data warehousing tools like Google BigQuery, and data visualization tools like Superset, Looker, Metabase, and Tableau. Key Responsibilities: - Develop and maintain ETL/ELT pipelines for efficient data processing - Utilize cloud data platforms, specifically GCP, for data storage and processing - Work with a variety of programming languages, databases, big data tools, cloud platforms, data warehousing solutions, and data visualization tools - Collaborate with cross-functional teams to ensure data quality and integrity - Stay updated on the latest technologies and tools in data engineering and implement best practices Qualifications Required: - Minimum 5 years of experience in data engineering or related roles - Proven experience with ETL/ELT pipeline development - Proficiency in Python, SQL, and preferably Golang - Experience with cloud data platforms, especially GCP - Familiarity with databases such as PostgreSQL, MySQL, and Redis - Knowledge of big data tools like Apache Spark, Apache Kafka, and Apache Airflow - Strong problem-solving and analytical skills - Excellent communication and collaboration abilities - Ability to work independently and in team environments - Attention to detail and focus on data quality - Continuous learning mindset Additional Details: The company emphasizes the importance of working with machine learning pipelines, MLOps, data governance, and data catalog tools. Familiarity with business intelligence tools like Tableau, Power BI, and Looker is preferred. Experience using AI-powered tools for coding acceleration, task automation, and system design assistance is valued at the company. The company believes in leveraging machine capabilities to enhance productivity and efficiency in data engineering processes.