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Tier-1 bank, metro data-engineer role with broad Spark/Python requirements increases candidate competition.
Technical data engineering skills are transferable, though banking domain experience may moderately influence fit.
Explicit 8-10 years plus mandatory Spark, Python, SQL and cloud/Databricks skills make shortlisting strict.
Design and maintain scalable ETL/ELT data pipelines and architectures including data lakes, warehouses, and lakehouse patterns.
Implement and oversee data quality, validation, and monitoring frameworks to ensure accurate and compliant data.
Optimize data solutions on cloud platforms using Spark, Kafka, Databricks and tune SQL/Spark jobs for efficiency and cost effectiveness.
8-10 years of experience in Data Engineering.
Proficiency in Python and strong SQL skills mandatory.
Hands-on experience with Apache Spark, Hadoop, or equivalent distributed computing frameworks.
Bachelor's degree or equivalent experience.
Demonstrated ability to build robust, scalable data engineering solutions from scratch in complex environments.
Experience working with large-scale distributed data systems and cloud-based data platforms.
Familiarity with both relational databases and NoSQL systems; knowledge of Machine Learning/AI considered an advantage.