Data Engineer

Role Overview We are looking for a highly skilled Data Quality Engineer with strong Data Engineering expertise to ensure the accuracy, reliability, and scalability of enterprise data platforms. The ideal candidate will possess hands-on experience with Databricks, PySpark, Hadoop, Hive, and Cloud technologies, along with advanced SQL skills to validate data across large-scale data pipelines and Lakehouse architectures. Required Experience 3+ years of experience in Data Engineering, Data Quality Engineering, or Data Testing. Hands-on experience with Databricks and PySpark. Strong experience in Hadoop ecosystem components such as Hive, HDFS, Spark, and related Apache technologies. Advanced SQL expertise for large-scale data validation and analysis. Experience working with Data Warehouses, Data Lakes, and Lakehouse architectures. Understanding of Star Schema, Snowflake Schema, and dimensional modeling. Experience with cloud platforms (Azure, AWS, or GCP). Requirements Key Responsibilities Data Quality & Validation Perform end-to-end validation of data pipelines across ingestion, transformation, and consumption layers. Execute source-to-target reconciliation and data quality checks. Identify, investigate, and resolve data anomalies and inconsistencies. Define and implement data quality frameworks, metrics, and controls. Data Engineering & Processing Develop and validate data pipelines using PySpark and Databricks. Work with large-scale datasets in Hadoop, Hive, and Lakehouse environments. Support ETL/ELT workflows and ensure data integrity throughout the data lifecycle. Optimize data processing jobs for performance and scalability. SQL & Analytics Write advanced SQL queries for data profiling, reconciliation, and root cause analysis. Perform complex joins, window functions, CTEs, aggregations, and query optimization. Validate business rules and transformation logic against source systems. Lakehouse & Cloud Platforms Validate and monitor data across Databricks Lakehouse architecture. Work with cloud platforms such as Azure, AWS, or GCP. Collaborate with Data Engineers, Architects, and Analysts to ensure reliable data delivery. Defect & Incident Management Analyze production issues and conduct root cause analysis. Track and manage data defects through resolution. Implement proactive monitoring and automated quality checks. Required Experience 6+ years of experience in Data Engineering, Data Quality Engineering, or Data Testing. Hands-on experience with Databricks and PySpark. Strong experience in Hadoop ecosystem components such as Hive, HDFS, Spark, and related Apache technologies. Advanced SQL expertise for large-scale data validation and analysis. Experience working with Data Warehouses, Data Lakes, and Lakehouse architectures. Understanding of Star Schema, Snowflake Schema, and dimensional modeling. Experience with cloud platforms (Azure, AWS, or GCP). Preferred Skills Automated data testing frameworks. Data observability and monitoring tools. CI/CD implementation for data pipelines. Experience with Delta Lake, Unity Catalog, or similar technologies. Knowledge of Airflow, Kafka, or other Apache ecosystem tools.