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.