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Tier-1 brand, mid-level data engineer role in metro increases applicant density and competition.
Big-data engineering skills transfer broadly, though payments governance adds moderate domain specificity.
Explicit 5+ years and mandatory big-data technology expertise make screening stringent.
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Design, build, and maintain scalable ETL/ELT data pipelines on big data platforms (Hadoop and enterprise systems) for analytics and reporting.
Ensure pipeline reliability, performance, data quality, governance, and support data governance/security including PII handling.
Collaborate with Product, Data Science, and Platform teams to deliver curated, high-quality datasets and support production troubleshooting and CI/CD practices.
5+ years of relevant experience in data engineering or big data analytics engineering (flexible based on depth of expertise).
Strong hands-on experience with Hadoop ecosystem (HDFS, Hive, Impala, YARN, Oozie) and/or cloud data platforms.
Proficiency in PySpark, Python, SQL; experience with orchestration tools such as Apache Airflow, NiFi, Azure Data Factory, Pentaho, or Talend.
Bachelor’s degree in computer science, Engineering, or equivalent practical experience.
Experienced in production-grade big data pipeline development with strong focus on performance optimization and data governance.
Skilled at collaborating cross-functionally to translate business requirements into reusable, governed data models and datasets.
Operates well in environments requiring CI/CD discipline, data quality automation, and clear communication with technical and non-technical stakeholders.