Back
Tier-1 brand, popular Data Engineer title, metro location and broad skillset increase applicant competition.
Core data engineering skills are transferable but payments/fraud domain knowledge is preferred.
Mandatory domain expertise and specific platform/tech requirements (Databricks, Hadoop, PySpark) raise strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead end-to-end data ingestion, aggregation, and processing to enable actionable insights across multiple data sources for cyber products and services.
Utilize platforms like Cloudera Data Engineering, Cloudera AI, Databricks, and AWS to develop scalable data engineering solutions.
Collaborate with cross-functional teams including Product, Data Science, and Technology to meet data requirements and deliver business-impacting solutions.
Proficient in Python (Pandas, Numpy, PySpark) and SQL; experience with data analysis and testing on Hadoop and Databricks Cloud platforms.
Experience in data engineering technologies including Cloudera, Databricks, AWS.
Work Experience Required: Not explicitly mentioned in the JD.
Experience with data modeling, programming, querying, and integrating large volumes of data to deliver business intelligence.
Experienced in integrating complex data assets and driving data unification across multiple sources to support innovative product development.
Able to translate data requirements across technical and business teams, supporting product and analytical solution delivery in fast-paced environments.
Familiarity with advanced data tools and concepts such as machine learning algorithms, ETL/ELT tools, and data visualization to enhance business decision-making.