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Tier-1 brand, mid-level generalist data role, metro location, and broad skill requirements drive high competition.
Core data engineering skills are transferable, though payments domain and governance add moderate industry specificity.
Explicit years plus many mandatory data engineering 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 on big data platforms (Hadoop ecosystem).
Ensure data pipeline reliability, quality, governance, and performance optimization including batch and streaming workloads.
Collaborate with product, data science, and platform teams to translate requirements into governed, reusable data models and support data governance, privacy, and security compliance.
2.5 - 4 years of relevant experience in data engineering or big data analytics engineering.
Proficiency in PySpark/Spark, Python, and SQL; experience with Hadoop ecosystem components (HDFS, Hive, Impala, YARN, Oozie).
Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
Experience with orchestration tools (e.g., Apache Airflow, NiFi) and knowledge of data modeling and incremental processing patterns (CDC, SCD Type 1/2).
Experienced in building production-grade big data pipelines focused on data quality, governance, and performance optimization.
Familiarity with cloud data platforms (Azure/AWS/GCP), columnar/open table formats (Parquet, ORC, Delta Lake), and CI/CD DevOps practices.
Capable of communicating complex technical data platform concepts effectively to both technical and non-technical stakeholders.