Data Engineer

Job Summary

Opportunity to work on real-world technology and SaaS products. Work with modern cloud-based data engineering technologies. Exposure to large-scale data pipelines and analytics. Collaborative and growth-oriented work environment. Opportunities for learning and career development. Strong proficiency in Python for data processing and automation. Experience with Cloud Storage (GCS). Experience with Cloud Composer / Apache Airflow. Knowledge of Dataflow or other distributed data processing tools. Experience with Looker Studio, Power BI, or Tableau. Knowledge of data lake and data warehouse architecture. Experience with Git/GitHub and CI/CD practices. Familiarity with cloud-based data security and access management. 3-5 years of professional experience in Data Engineering. Strong hands-on experience with ETL/ELT processes. Advanced knowledge of SQL and database concepts. Strong experience with Google Cloud Platform (GCP). Hands-on experience with Google BigQuery. Good understanding of data warehousing and data modeling. Experience building and managing data pipelines. Experience working with relational and cloud databases. Experience integrating data through APIs. Strong understanding of data transformation, cleansing, and validation. Good problem-solving and analytical skills. Design, develop, and maintain scalable ETL/ELT pipelines. Extract, transform, and load data from databases, APIs, files, and other sources. Develop efficient and optimized SQL queries for data processing and analysis. Work with Google Cloud Platform (GCP) and its data engineering services. Build and manage data pipelines using Google BigQuery and related GCP services. Perform data loading, transformation, cleansing, and validation. Design and maintain data warehouse structures and data models. Integrate data from multiple internal and external sources. Develop data workflows and automate recurring data processing tasks. Monitor pipeline performance, troubleshoot failures, and ensure data availability. Optimize queries and pipelines for performance and cost efficiency. Implement data quality checks and ensure accuracy and consistency of data. Work with APIs and different data formats such as JSON, CSV, and XML. Collaborate with developers, product teams, analysts, and business stakeholders to understand data requirements. Maintain technical documentation for data pipelines, workflows, and data sources. Supplymint is looking for an experienced Data Engineer to design, develop, and maintain scalable data pipelines and data infrastructure. The ideal candidate should have strong hands-on experience in ETL/ELT, SQL, data loading, Google Cloud Platform, BigQuery, and data warehousing. The candidate will be responsible for building reliable data pipelines, integrating data from multiple sources, ensuring data quality, and supporting business and analytics requirements.

Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines.
• Extract, transform, and load data from databases, APIs, files, and other sources.
• Develop efficient and optimized SQL queries for data processing and analysis.
• Work with Google Cloud Platform (GCP) and its data engineering services.
• Build and manage data pipelines using Google BigQuery and related GCP services.
• Perform data loading, transformation, cleansing, and validation.
• Design and maintain data warehouse structures and data models.
• Integrate data from multiple internal and external sources.
• Develop data workflows and automate recurring data processing tasks.
• Monitor pipeline performance, troubleshoot failures, and ensure data availability.
• Optimize queries and pipelines for performance and cost efficiency.
• Implement data quality checks and ensure accuracy and consistency of data.
• Work with APIs and different data formats such as JSON, CSV, and XML.
• Collaborate with developers, product teams, analysts, and business stakeholders to understand data requirements.
• Maintain technical documentation for data pipelines, workflows, and data sources.

Requirements

• 3-5 years of professional experience in Data Engineering.
• Strong hands-on experience with ETL/ELT processes.
• Advanced knowledge of SQL and database concepts.
• Strong experience with Google Cloud Platform (GCP).
• Hands-on experience with Google BigQuery.
• Good understanding of data warehousing and data modeling.
• Experience building and managing data pipelines.
• Experience working with relational and cloud databases.
• Experience integrating data through APIs.
• Strong understanding of data transformation, cleansing, and validation.
• Good problem-solving and analytical skills.

Skills

• Python for data processing and automation
• Looker Studio
• Power BI
• Tableau
• Cloud Storage (GCS)
• Cloud Composer
• Google BigQuery
• Google Cloud Platform (GCP)
• APIs

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.