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Tier-1 brand, popular mid-level data engineer role with broad skills in a metro location increases competition.
Core data engineering skills transfer across industries, though financial risk domain familiarity increases fit specificity.
Explicit 5-8 years requirement plus mandatory PySpark, SQL and distributed systems implies high shortlisting strictness.
Design, develop, and optimize large-scale data pipelines and ETL/ELT workflows using PySpark and big data technologies.
Build and maintain scalable backend systems and APIs with Python, ensuring code quality and performance optimization.
Participate in end-to-end SDLC activities including requirement analysis, development, testing, deployment, and troubleshooting production incidents.
5-8 years of hands-on experience as a PySpark developer with real-time project delivery experience.
Strong expertise in Python, PySpark, and SQL with advanced querying and performance tuning skills.
Experience with Hadoop ecosystem, Spark, Impala, Hive, and relational databases such as SQL Server.
Bachelor’s degree in any specialization.
Experienced in handling large-scale data processing, pipeline lifecycle management, and performance optimization challenges.
Proficient in distributed data systems and cloud platforms (Azure, AWS, or GCP).
Familiar with SDLC methodologies (Agile/Scrum), version control (Git), and CI/CD pipelines.