Job description

Job Summary We are seeking a highly skilled and experienced Senior Data Engineer with a strong background in Databricks, SQL, and Python, pyspark to join our data engineering team. The ideal candidate will have a proven track record of designing, building, and deploying scalable data pipelines and solutions in cloud environments. You will be responsible for end-to-end development, from data ingestion to deployment, ensuring high performance and reliability. Key Responsibilities • Design, develop, and maintain scalable data pipelines using Databricks and Apache Spark. • Write efficient and optimized SQL queries for data extraction, transformation, and analysis. • Develop robust data processing scripts and automation using Python, Pyspark. • Implement end-to-end data solutions including ingestion, transformation, storage, and deployment. • Collaborate with data scientists, analysts, and business stakeholders to understand data requirements. • Optimize data workflows for performance, scalability, and reliability. • Ensure data quality, integrity, and governance across all stages of the pipeline. • Monitor and troubleshoot production data pipelines and deployments. • Document technical designs, processes, and best practices. Required Qualifications • 5+ years of professional experience in data engineering or related roles. • Strong proficiency in Databricks, SQL, and Python, Pyspark. • Experience with end-to-end deployment of data solutions in cloud environments (e.g., Azure, AWS, GCP). • Solid understanding of ETL/ELT processes, data modeling, and data warehousing concepts. • Familiarity with CI/CD pipelines, version control (Git), and workflow orchestration tools (e.g., Airflow). • Experience with structured and unstructured data formats (e.g., Parquet, JSON, CSV). • Strong problem-solving skills and attention to detail. • Excellent communication and collaboration skills. Preferred Qualifications • Experience with Delta Lake or other Databricks ecosystem tools. • Knowledge of data governance, security, and compliance standards. • Familiarity with containerization (Docker) and Kubernetes. • Exposure to real-time data processing (e.g., Kafka, Spark Streaming).

Requirements