Role DescriptionThe Data Engineer is responsible for designing, building, and maintaining scalable data pipelines and architectures that enable efficient data collection, processing, and analysis. This role ensures that high-quality, reliable data is available to support business intelligence, analytics, and machine learning initiatives. The ideal candidate is technically strong, detail-oriented, and passionate about building robust data systems that transform raw data into actionable insights.Key ResponsibilitiesDesign, develop, and optimize data pipelines, ETL/ELT processes, and workflows for structured and unstructured data.Build and maintain scalable data architectures that support data warehousing, analytics, and reporting needs.Integrate data from multiple sources such as APIs, databases, and third-party systems into centralized data platforms.Collaborate with data analysts, data scientists, and business teams to understand data requirements and ensure data accuracy and availability.Develop and enforce best practices for data governance, security, and quality assurance.Monitor, troubleshoot, and optimize data processes for performance and cost efficiency.Implement data validation, cleansing, and transformation procedures to maintain data integrity.Work with cloud platforms (e.g., AWS, Azure, GCP) to manage data storage, orchestration, and automation tools.Create and maintain documentation for data models, data flow diagrams, and pipeline configurations.Support the development of analytics and machine learning pipelines by providing clean and well-structured datasets.Collaborate with DevOps teams to deploy, scale, and maintain data infrastructure in production environments.Continuously improve data engineering practices through automation, monitoring, and innovation.Stay updated on emerging technologies and trends in data architecture, big data, and cloud computing.QualificationsBachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, or a related field.2–5 years of experience in data engineering, data warehousing, or database development.Strong proficiency in SQL and at least one programming language (Python, Java, or Scala preferred).Hands-on experience with ETL tools and frameworks (e.g., Apache Airflow, dbt, or Talend).Experience with big data technologies such as Spark, Hadoop, or Kafka.Familiarity with cloud-based data services (AWS Redshift, Google BigQuery, Azure Synapse, or Snowflake).Solid understanding of data modeling, schema design, and database management (relational and NoSQL).Knowledge of APIs, data integration, and data streaming methodologies.Strong problem-solving, analytical, and debugging skills.Excellent collaboration and communication abilities to work cross-functionally.Experience with containerization tools (Docker, Kubernetes) and CI/CD pipelines is a plus.Commitment to building efficient, scalable, and reliable data systems that support business growth.