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Metro role, popular Data Engineer title, and broad PySpark/ETL skill requirements create high competition.
Specialized data engineering skills are transferable but banking/regulatory preference reduces cross-industry fit to medium.
Explicit 7+ years plus mandatory PySpark/Python, ETL, and data warehouse experience increases screening strictness.
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Design, develop, test, deploy, and support scalable ETL pipelines, data marts, and data warehousing solutions using Python, PySpark, SQL, and related technologies.
Analyze business and technical requirements to deliver reliable data processing solutions for structured, semi-structured, and unstructured data while ensuring data quality and pipeline performance.
Participate in full software development lifecycle activities including build, UAT, defect fixing, deployment, and post-production support, including debugging and resolving pipeline failures.
7+ years overall professional experience in data engineering, software development, or related roles.
5+ years commercial experience in data-driven roles with hands-on ETL pipeline and data mart development.
Strong expertise in Python, PySpark, Spark, Hadoop, Hive, SQL, Oracle queries, and experience with SQL and NoSQL databases.
Degree in Computer Science, Information Technology Engineering, or equivalent; certifications preferred.
Experienced in production-scale distributed data processing with strong ownership over ETL pipeline design and maintenance.
Skilled at working across full SDLC including collaboration with technical and business stakeholders to translate requirements and resolve issues.
Familiar with banking, financial services, or regulated data-intensive environments and knowledgeable about data governance, security, and operational best practices.