# Job Description: Data Engineering & Warehousing Engineer Job Title: Data Engineering & Warehousing Engineer Experience: 3–11 Years Location: Islamabad (On-site/Hybrid as per business requirement) Employment Type: Full-Time ## Job Overview We are seeking a highly skilled Data Engineering & Warehousing Engineer with 3–11 years of experience to design, develop, and maintain scalable data platforms and enterprise data warehouse solutions. The ideal candidate will have hands-on expertise in building ETL/ELT pipelines, data integration, cloud-based data platforms, and big data processing technologies. You will play a key role in enabling reliable, high-performance analytics and business intelligence solutions. ## Key Responsibilities - Design, develop, and optimize scalable ETL/ELT pipelines for structured and unstructured data. - Build and maintain enterprise data warehouses, data lakes, and modern data platforms. - Develop real-time and batch data processing solutions. - Integrate data from multiple internal and external sources while ensuring data quality and governance. - Collaborate with Data Scientists, BI Developers, and business stakeholders to support analytical requirements. - Optimize data storage, query performance, and pipeline reliability. - Implement data security, monitoring, and governance best practices. - Troubleshoot and resolve data pipeline and platform issues. - Participate in architecture discussions and contribute to data platform modernization initiatives. ## Required Technical Skills ### Cloud Data Platforms - Hands-on experience with Google BigQuery and Dataflow and Dataproc and Pub/Sub. - Experience with Azure Synapse and Azure Data Factory. - Experience with Amazon Redshift and AWS Glue. ### Data Processing & Streaming - Strong experience with Apache Spark and Apache Kafka. - Experience building batch and real-time data processing pipelines. ### Data Transformation - Hands-on experience with dbt or Oracle Data Integrator (ODI) for data transformation and orchestration. - Experience implementing ETL/ELT best practices and reusable data models. ### Databases & Data Warehousing - Strong experience with Oracle or PostgreSQL. - Expertise in SQL, relational database design, performance tuning, and query optimization. ### Data Engineering - Experience with data modeling, data governance, metadata management, and data quality frameworks. - Knowledge of dimensional modeling and modern data warehouse architectures. ## Qualifications - Bachelor's degree in Computer Science, Information Technology, Data Engineering, Software Engineering, or a related field. - 3–11 years of professional experience in Data Engineering, Data Warehousing, or Big Data technologies. - Strong programming and scripting skills using SQL, Python, or similar languages. - Excellent analytical and problem-solving abilities. - Experience working in Agile/Scrum development environments. ## Preferred Skills - Experience with cloud-native data lake and lakehouse architectures. - Knowledge of CI/CD pipelines and Infrastructure as Code (IaC). - Familiarity with containerization technologies such as Docker and Kubernetes. - Experience supporting machine learning and analytics workloads. - Cloud certifications on AWS, Microsoft Azure, or Google Cloud Platform are a plus. ## Key Technology Stack - Google Cloud Data Services: BigQuery and Dataflow and Dataproc and Pub/Sub - Azure Data Services: Azure Synapse and Azure Data Factory - AWS Data Services: Amazon Redshift and AWS Glue - Data Processing: Apache Spark and Apache Kafka - Data Transformation: dbt or Oracle Data Integrator (ODI) - Databases: Oracle or PostgreSQL - Programming: SQL and Python - Cloud Platforms: Google Cloud Platform or Microsoft Azure or Amazon Web Services (Preferred)