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Tier-1 brand, common data role, mid-level experience, metro location, broad skillset.
Core big-data engineering skills are transferable across industries, though payments domain adds moderate specialization.
Explicit 5+ years requirement and specific big-data tech stack increases filtering rigor.
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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 in big data environments.
Collaborate with cross-functional teams to translate requirements into governed data models and support operational reliability including troubleshooting production issues.
5+ years of experience in data engineering or big data analytics engineering.
Proficiency in PySpark, Python, SQL (including Impala) and hands-on experience with Hadoop ecosystem components (HDFS, Hive, Impala, YARN, Oozie).
Experience with data orchestration tools like Apache Airflow, Azure Data Factory, or equivalent.
Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
Experienced in building and maintaining production-grade big data pipelines with strong focus on data governance and quality.
Skilled at optimizing distributed data processing utilizing partitioning, columnar file formats (Parquet, ORC, Delta), and compute tuning.
Able to engage effectively with both technical teams (data scientists, platform teams) and non-technical stakeholders to clarify capabilities and requirements.