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Mid-tier brand, Bangalore location, and common PySpark/data-engineer skills create moderate competition.
Role targets financial index analytics so domain experience moderately affects fit despite transferable data skills.
Explicit 7–12 years requirement plus mandatory PySpark, Python, cloud and big-data tech increases shortlisting strictness.
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Design, develop, and optimize large-scale, scalable PySpark data pipelines for financial and index data in batch and streaming environments.
Lead the building and deployment of machine learning pipelines, including feature engineering, training, validation, and model deployment.
Collaborate with data scientists and stakeholders to convert data into actionable insights and maintain automation scripts for data processing and model validation.
7-12 years of experience in data engineering, big data processing, or data science with strong hands-on expertise in PySpark and Python.
Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or related field.
Experience with SQL/NoSQL databases, Spark components (Spark SQL, DataFrames, Spark MLlib), and ETL tools like Apache Airflow, Jenkins, or GitHub Actions.
Experience with cloud platforms such as Azure or AWS for big data processing.
Proven track record of building scalable data pipelines and supporting ML workflows within enterprise financial or index analytics domains.
Experience leading or mentoring technical teams, indicating leadership capability in project execution.
Comfortable working with structured, semi-structured, and unstructured data and automating data ingestion, transformation, and validation processes.