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Tier-1 brand, popular Data Engineer title, metro location, and broad tech stack increase competition.
Core Spark, SQL and Python data engineering skills are highly transferable across industries despite payments context.
Explicit experience band plus mandatory Databricks/Spark/Python/SQL/Airflow skills and degree raise filter strictness.
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Develop and maintain data capabilities and infrastructure for Mastercard's Sustainable Technology Internal Data Lake and related data products.
Design and optimize data pipelines, implement data transformation and integration processes supporting analytics and reporting.
Collaborate with cross-functional teams to align on data requirements, establish best practices, and ensure data quality, security, and accessibility.
Bachelor’s degree in Computer Science, Engineering, Data Science, or related field.
0.6 - 1.5 years of experience in Data Engineering or Data Warehouse related projects.
Hands-on experience with data technologies such as Databricks, Spark, Python, SQL, Hadoop, and Airflow.
Familiarity with cloud platforms like AWS or Azure and strong SQL skills.
Proven experience implementing end-to-end data engineering or warehouse projects in Big Data environments.
Skilled in building data pipelines using Spark with Scala/Python/Java in Databricks or Hadoop ecosystems.
Experience working in Agile teams and automating data workflows using Airflow or similar tools.