Data Engineer (Onsite, Lahore, PKR Salary)

HR POD Careers


Date: 3 weeks ago
City: Lahore
Contract type: Full time
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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