Senior Python Developer

• Lead hands-on implementation and architecture of Python frameworks for scalable, resilient, and performant applications.
• Develop solutions adhering to architectural standards, utilizing in-depth technical and business domain knowledge.
• Design and implement agentic flows and build autonomous AI agents capable of reasoning, planning, and tool utilization.
• Enforce industry-standard SDLC best practices and contribute to code quality through comprehensive code reviews.
• Collaborate effectively with cross-functional teams (Risk, Quants, FO, DevOps, Production Support) to ensure seamless project delivery.
• Drive development for Stress Testing and Regulatory Risk projects, specifically within the Market Risk domain.
• Manage project technical aspects, including planning, governance, and taking ownership of initiatives from conception to completion.
• Deliver high-quality solutions independently within a globally matrixed environment and under tight deadlines.

• Expert-level programming skills in Python and PySpark, specifically for developing Spark applications.
• Experience with PySpark for data processing.
• Familiarity with data manipulation libraries (Pandas, NumPy).
• Scripting for automation and data orchestration.
• Complex query writing, including subqueries, window functions, and performance tuning.
• Strong proficiency in Spark Core, Spark SQL, Spark Streaming.
• Experience with Spark performance optimization techniques (e.g., caching, partitioning, shuffle optimizations, memory management).

Preferred Skills:

• Experience with Spark GraphX.
• In-depth understanding of HDFS architecture, data storage, and fault tolerance mechanisms.
• Experience with HDFS commands and administration.
• Solid understanding of YARN resource management and job scheduling.
• Fundamental understanding of the MapReduce programming paradigm.
• Knowledge of Zookeeper for distributed coordination services.
• Experience with Big Data Technologies (Apache Spark, Hadoop, Kafka) and data warehousing.
• Experience with HBase for real-time access to large datasets within Hadoop.
• Experience with NoSQL databases such as Cassandra, MongoDB, or similar.
• Familiarity with RDBMS concepts and SQL for data integration.
• Understanding of dimensional modeling, fact and dimension tables, and star/snowflake schemas.