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Tier-1 brand, metro location, and common Data Engineer title increase competition.
Core data engineering skills transfer across industries, though payments experience is advantageous.
Multiple mandatory technical skills (Spark, AWS, Databricks, SQL, Python) and seniority imply high filter strictness.
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Design, build, and maintain scalable batch and near-real-time data pipelines for analytics, BI, and ML workloads.
Develop curated datasets and data models enabling reliable analytics and machine learning feature engineering.
Implement pipeline reliability patterns including monitoring, alerting, retries, and performance tuning, ensuring secure data handling aligned with governance.
Strong proficiency in SQL and Python (or Scala) for data engineering tasks.
Experience building data pipelines using distributed processing frameworks like Spark, PySpark, or Hadoop.
Experience working with cloud data ecosystems, preferably AWS, including object storage and lakehouse patterns.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in designing and operating production-grade, reliable data pipelines and feature engineering workflows in cloud environments.
Proficient in data modeling techniques (dimensional and lakehouse), analytics, and ML integration with strong troubleshooting skills.
Familiar with data governance, security compliance, and working in regulated or payments/banking environments.