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Tier-1 brand, generalist senior data engineer title, and metro location increase applicant competition.
Data engineering skills transfer across industries but payments/fraud focus increases domain relevance.
Specific tech stack (PySpark, Databricks, SQL) required but no explicit years, so medium strictness.
Perform data ingestion, aggregation, processing across multiple platforms (Cloudera Data Engineering, Databricks, AWS) to enable insights and product engineering solutions.
Collaborate with cross-functional teams (Product, Data Science, Platform Strategy, Technology) to deliver data solutions generating business value.
Develop prototypes and integrate new data assets to enhance cyber products, services, and actionable insights from complex, high-volume datasets.
Proficient in Python (Pandas, Numpy, PySpark) and SQL; experience with Hadoop and Databricks Cloud platform required.
Hands-on experience with data modeling, programming, querying, and report development using large volumes of granular data.
Knowledge of data mining, machine learning algorithms, and tools mandatory; experience with ETL/ELT tools and/or Graph Database is a plus.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in cloud-based big data environments and capable of delivering data-driven solutions in fast-paced settings.
Able to bridge technical and business domains, articulating solution requirements across diverse teams.
Demonstrated ability to innovate, prototype, and improve existing data products focusing on cyber security and digital identity domains.