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Strong Tier-1 brand, mid-level generalist data role, metro locations, and broad skill requirements drive high competition.
Core data engineering skills like SQL, Spark, Python are highly transferable across industries.
Explicit 4–9 years requirement plus many mandatory technologies (Databricks, Oracle, Spark, Python) increases shortlisting strictness.
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Design and develop data infrastructure, ETL/ELT pipelines, data integration, and transformation solutions to enable efficient data processing and actionable insights.
Build and maintain data models and administer databases including Oracle, MongoDB, relational DBs, and lakehouse platforms like Databricks with Spark SQL, Delta Lake.
Support AI use cases through preparation of AI-ready data pipelines and troubleshooting production issues while mentoring junior team members and collaborating across teams.
Bachelor degree minimum.
4-9 years of experience in data engineering or relevant field.
Proficiency in English (oral and written).
Strong skills in Oracle Database, SQL, PL/SQL, along with knowledge of MongoDB, Databricks, Apache Spark, PySpark, and Python scripting.
Experienced data engineer familiar with complex relational and NoSQL databases, ETL pipeline design, and performance tuning.
Capable of working with modern data lakehouse platforms and supporting AI/ML data workflows including embedding, vector search, and RAG.
Comfortable troubleshooting production systems, mentoring juniors, and collaborating with diverse internal stakeholders to align data solutions with business goals.