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.

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