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Strong Mastercard brand, popular senior data role, and broad skill requirements increase applicant competition.
Core data engineering skills (Python, Spark, Databricks, SQL) are highly transferable across industries.
Multiple mandatory platform and tooling skills (Databricks, Hadoop, PySpark, SQL) create moderate shortlisting rigidity.
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Lead data ingestion, aggregation, and processing using platforms like Cloudera Data Engineering, Databricks, and AWS to generate actionable insights.
Collaborate with product managers, data science, and technology teams to deliver unified data solutions that drive business value and support innovative cyber security products.
Analyze large volumes of complex transaction and product data to identify opportunities for new products, services, and enhancements with measurable business impact.
Proficient in Python (Pandas, Numpy, PySpark), SQL, and data engineering on Hadoop and Databricks Cloud platforms.
Experience with data mining, machine learning algorithms, and handling high-volume, high-dimensional datasets.
Work Experience Required: Not explicitly mentioned in the JD.
Exposure to data modeling, querying, analytics, and business intelligence techniques is required.
Experienced in engineering data solutions that unify and curate multiple data sources for real-time risk, security, and fraud analytics.
Capable of working effectively across technical teams and business units to translate complex requirements into data products.
Comfortable operating in a fast-paced environment balancing product innovation, data platform strategy, and stakeholder collaboration.