Data Engineer – Python, PySpark, SQL & ETL

Job Summary Synechron is seeking a Data Engineer with 5+ years of experience in building data marts, ETL pipelines, and large-scale data-processing solutions. The role will focus on designing, developing, optimizing, and supporting scalable data pipelines using Python, PySpark, Spark, SQL, and data-warehousing technologies. The position will cover the end-to-end software development lifecycle, including development, testing, UAT support, production deployment, issue resolution, and post-production support. The role will contribute to business objectives by delivering reliable and efficient data solutions, improving data quality and accessibility, supporting informed decision-making, and enabling consistent data operations across enterprise environments. Software Requirements Required Software Skills Python: Strong hands-on experience developing data-processing applications, ETL workflows, automation scripts, and production services. PySpark and Spark: Strong experience building distributed data-processing and ETL solutions. Pandas: Experience with data preparation, analysis, transformation, validation, and profiling. SQL: Strong experience with data extraction, transformation, validation, analysis, and query optimization. Oracle SQL: Strong knowledge of Oracle SQL, complex queries, joins, aggregations, subqueries, stored procedures, and performance optimization. ETL development: Experience designing, developing, testing, deploying, and supporting ETL and data-pipeline solutions. Data warehousing: Strong understanding of data-warehouse concepts, dimensional modelling, data marts, transformations, fact tables, dimensions, and data quality. Hadoop, Hive, and MapReduce: Experience working with distributed data-processing and big-data technologies. SQL and NoSQL databases: Experience working with structured, semi-structured, and unstructured data. Jupyter Notebook: Experience using notebooks for data analysis, development, validation, troubleshooting, and technical exploration. CI/CD pipelines: Familiarity with automated build, testing, deployment, and release processes. Testing and data validation: Experience validating data transformations, pipeline results, data quality, and application behavior. Software engineering practices: Understanding of coding standards, version control, documentation, code reviews, testing, and maintainable development practices. Preferred Software Skills Experience with banking or financial-services data and reporting requirements. Exposure to cloud-based data platforms and data-processing services. Familiarity with workflow scheduling, orchestration, monitoring, and alerting tools. Experience with automated data-quality checks, reconciliation, lineage, and metadata management. Exposure to infrastructure automation, containerization, and modern DevOps practices. Familiarity with tools supporting data pipeline observability and performance analysis. Version and Experience Guidance