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Tier-1 brand, mid-level data title, metro location, and broad skillset drive high applicant competition.
Core big-data skills transfer across industries, but payments-specific governance and PII handling increase sensitivity.
Explicit 5+ years plus mandatory Hadoop, PySpark, SQL, and governance skills indicate high shortlisting strictness.
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Design, build, and operate scalable ETL/ELT data pipelines and curated datasets for analytics, reporting, and advanced modeling.
Ensure data quality, governance, and performance optimization across batch and streaming workloads on big data platforms (Hadoop ecosystem and cloud).
Collaborate with cross-functional teams to translate requirements into reusable data models and maintain CI/CD practices for data workflows, support production issue troubleshooting and operational reliability.
Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
5+ years of relevant experience in data engineering or big data analytics engineering.
Proficiency in PySpark, Python, SQL, and experience with Hadoop ecosystem components (HDFS, Hive, Impala, YARN, Oozie).
Experience with data orchestration tools (e.g., Apache Airflow, NiFi) and knowledge of data governance, quality, and security practices.
Experienced in building and optimizing production-grade big data pipelines with strong understanding of distributed computing and data modeling patterns.
Skilled at collaborating with product, data science, and platform teams to deliver governed, curated datasets ready for analytics and AI use cases.
Familiar with cloud data platforms (Azure/AWS/GCP), DevOps/CI-CD practices, and able to communicate complex technical concepts to both technical and non-technical stakeholders.