Back
Tier-1 brand plus popular mid-level data engineer role with broad skillset and metro hiring increases applicant density.
Core data engineering skills transfer across industries, though payments-specific governance and domain knowledge increase sensitivity.
Explicit 2.5–4 years requirement plus mandatory big-data tech and domain experience increases shortlist rigidity.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build, and operate scalable production-grade data pipelines and curated datasets powering analytics, reporting, and advanced modeling.
Ensure reliability, performance, data quality, governance, and security of batch and streaming data workloads across enterprise data platforms.
Collaborate with cross-functional teams to translate requirements into governed data models and support AI/ML data readiness, including troubleshooting and operational support.
2.5 to 4 years of experience in data engineering or big data analytics engineering.
Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
Strong hands-on skills in PySpark, Python, SQL, and Hadoop ecosystem technologies (HDFS, Hive, Impala, YARN, Oozie).
Experience with ETL/ELT pipeline development, data modeling, data quality automation, and cloud data platforms (Azure/AWS/GCP preferred).
Experienced in building performant, scalable data pipelines with expertise in distributed computing optimization and data governance.
Able to translate complex data requirements into reusable, governed semantic data layers for analytics and AI use cases.
Proficient in DevOps/CI-CD practices and able to communicate technical concepts effectively to both technical and non-technical stakeholders.