Data Engineer

🛠️ Key Responsibilities Design, develop, and maintain scalable data pipelines and ETL/ELT processes. Build and optimize enterprise data warehouses and data lake solutions. Develop high-performance data processing solutions using Spark/PySpark. Integrate data from multiple internal and external sources. Optimize SQL queries and improve data pipeline performance. Implement batch and real-time data processing solutions. Work with Data Scientists, BI Developers, and Software Engineers to support analytics initiatives. Ensure data quality, governance, security, and compliance. Deploy and maintain cloud-based data solutions. Perform code reviews and mentor junior team members. Troubleshoot production issues and implement performance improvements. Follow Agile development methodologies and DevOps best practices. ✅ Required Skills Category Skills Experience 12+ Years in Data Engineering Programming Python, SQL, PySpark, Scala (Preferred) Big Data Apache Spark, Hadoop, Hive Data Integration ETL/ELT, Apache Airflow Streaming Apache Kafka Cloud AWS / Azure / GCP Data Warehouse Snowflake, Redshift, BigQuery, Azure Synapse Data Lake Delta Lake, Apache Iceberg Databases PostgreSQL, MySQL, SQL Server, MongoDB DevOps Git, Docker, Kubernetes, Jenkins Operating System Linux / Unix APIs REST APIs Data Modeling Star Schema, Snowflake Schema, Dimensional Modeling Performance SQL Optimization, Spark Performance Tuning Methodology Agile / Scrum 🎓 Qualifications Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related discipline. Relevant cloud or data engineering certifications are an advantage. ⭐ Preferred Skills Experience with Databricks. Experience with dbt and Terraform. Knowledge of CI/CD pipelines. Experience with real-time streaming architectures. Exposure to machine learning data pipelines.