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Tier‑1 brand, metro location, and popular mid-level data engineer skillset increase competition.
Core PySpark, Python and Big Data skills are broadly transferable across industries despite banking specifics.
Mandatory Big Data tech stack, explicit years requirement, and banking controls make shortlisting strict.
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Lead applications systems analysis and programming activities in collaboration with Technology teams for new or revised application systems.
Ensure application design aligns with architecture blueprint while developing coding, testing, debugging, and implementation standards.
Analyze complex business and system processes to resolve high-impact problems and coach mid-level developers and analysts.
At least 2 years of experience with Big Data technologies (HDFS, MapReduce, YARN, Apache Spark, Hive).
Proficiency in PySpark/Python development including performance tuning and troubleshooting in Big Data environments.
Bachelor’s degree in Computer Science or equivalent experience; Master’s preferred.
Experience with UNIX/Linux, shell scripting, SQL, and understanding of scalable application design principles.
Experienced in end-to-end application development lifecycle including design, unit testing, and implementation within Big Data ecosystems.
Comfortable working with large data volumes and multiple data formats (Avro, Parquet, CSV, JSON).
Familiarity with Agile/Scrum practices, source control (GIT), continuous integration tools, and real-time data processing (Kafka) is advantageous.