Data Engineer (Onsite, Lahore, PKR Salary)

****Requirements:**** * 3\+ years of experience in Data Engineering or related roles. * Strong expertise in SQL, query optimization, and database performance tuning. * Proven experience in designing, building, and maintaining ETL/ELT pipelines. * Hands\-on experience with Databricks or similar large\-scale data processing platforms. * Experience working with cloud data warehouses such as Snowflake, BigQuery, or Redshift. * Proficiency in Python for data processing, scripting, and automation. * Familiarity with workflow orchestration tools such as Airflow or similar platforms. * Strong understanding of data modeling, data warehousing, and scalable architecture design. * Experience handling large datasets and distributed data systems. * Excellent problem\-solving, troubleshooting, and debugging skills. * Experience with Spark, Kafka, or real\-time data streaming technologies. * Exposure to healthcare or SaaS\-based datasets is a plus. * Knowledge of data governance, metadata management, and data quality practices. * Familiarity with DevOps practices, CI/CD pipelines, and infrastructure automation. * Experience supporting and maintaining machine learning pipelines. ****Responsibilities:**** * Design, develop, and maintain scalable ETL/ELT pipelines using internal systems, APIs, and external data sources. * Build and optimize data architectures, warehouses, and storage solutions for scalability and performance. * Clean, validate, and transform raw data to support analytics, reporting, and business intelligence needs. * Write, optimize, and maintain complex SQL queries and data models. * Monitor and manage data workflows to ensure performance, reliability, and scalability. * Ensure data quality, integrity, consistency, and security across all systems and pipelines. * Collaborate closely with analysts and stakeholders to support reporting and business intelligence requirements. * Automate repetitive data processes and continuously improve workflow efficiency. * Maintain clear documentation for data pipelines, datasets, schemas, and infrastructure. * Continuously evaluate and enhance the company's data stack, architecture, and engineering best practices.