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Tier-1 bank brand, common Data Engineer title, metro location, and mid-level experience amplify competition.
Core data engineering skills are transferable, though finance domain preference raises relevance requirements moderately.
No explicit years but required Databricks/PySpark skills and finance domain preference imply moderate filtering.
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Build and optimise data pipelines and data models to support business analytics and AI initiatives.
Extract, transform, and automate data processes primarily using SQL and Python; leverage cloud-based platforms such as Databricks.
Deliver data engineering solutions within financial services, focusing on credit card or personal loan product data.
Proficient in SQL and Python; knowledge of PySpark is advantageous.
Experience with Databricks or similar cloud data platforms.
Work Experience Required: Experience in data engineering or analytics roles within financial services, preferably with exposure to credit card or personal loan products.
Not explicitly mentioned: mandatory degree, notice period, or onsite/location restrictions beyond DLF Downtown, Sector 25A Phase 3, Block 4 location.
Experienced in delivering scalable data solutions that enable AI and advanced analytics within financial services.
Demonstrated ability to work independently with minimal supervision and collaboratively in teams.
Familiarity or certification in Databricks Data Engineer and exposure to AI engineering, SAS, or Power BI increases competitiveness.