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Strong Tier-1 brand, popular Data Engineer title, and broad cloud/big-data requirements increase applicant competition.
Core data engineering skills like Spark, SQL, and cloud are highly transferable across industries.
Medium due to senior title and mandatory cloud, big-data, and ML pipeline expertise.
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Design, build, and maintain scalable data pipelines and ETL/ELT processes integrating multiple data sources for merchant identity resolution and enriched datasets.
Support deployment, integration, and monitoring of AI/ML model features and artifacts into production systems to ensure reliability and performance.
Collaborate cross-functionally with data science, engineering, and governance teams to comply with security standards and optimize data workflows in distributed big data/cloud environments.
Experience working with big data platforms like Databricks and Apache Spark at scale.
Proficient in Python and SQL coding with ability to write clean, maintainable code.
Bachelor’s degree in Computer Science, Data Analytics, Mathematics, Software Engineering or equivalent practical experience.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in end-to-end ML model development workflows and entity-centric data systems in production-scale environments.
Demonstrates ability to independently research and implement solutions using cloud platforms such as Databricks, AWS, or GCP.
Comfortable collaborating across data science, engineering, and governance teams with a focus on platform standardization and production readiness of AI/ML models.