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Tier-1 brand, popular data-engineer title, metro location, and broad tech requirements drive high competition.
Core data engineering skills are transferable, but PCI/payments regulatory experience increases domain specificity.
Explicit 6+ years plus many mandatory tools (Snowflake, Databricks, PCI, Airflow) makes shortlisting highly strict.
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Design, develop, and maintain scalable enterprise data pipelines and platforms supporting business intelligence, fraud analytics, ML initiatives, and operational reporting.
Ensure secure, scalable, compliant data solutions by partnering with business, product, architecture, and engineering teams.
Monitor and optimize data platform performance and implement data governance, security, and operational support processes, including incident management and disaster recovery.
Bachelor's degree in Computer Science, IT, Data Engineering, Software Engineering, or related field.
6+ years of experience in Data Engineering, Big Data, Data Warehousing, or Analytics Engineering.
Hands-on experience with Snowflake, Databricks, Apache Spark, Apache Airflow, and Python programming for data engineering.
Experience working in PCI-regulated environments handling sensitive customer data with knowledge of data governance, encryption, masking, and secure data processing.
Experienced in building large-scale, cloud-native data platforms leveraging Snowflake, Databricks, Spark, and Azure cloud services.
Proficient in designing robust ETL/ELT pipelines, data modeling, and managing large structured and semi-structured datasets for analytics and ML use cases.
Skilled in implementing enterprise data governance and security standards, working cross-functionally in global teams to deliver compliant, reliable data solutions.