Data Engineering & Warehousing Engineer

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. 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) Share 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.