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Mid-level data-engineer role in Bangalore with common skillset increases applicant competition.
Data engineering skills transfer across industries, but banking domain preference raises specificity moderately.
Explicit 5-year requirement plus mandatory Databricks, ADF and Kafka skills makes shortlisting strict.
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Design, develop, and maintain scalable data pipelines and ETL/ELT processes using Azure Data Factory, Databricks, SQL, and Spark.
Implement batch and real-time data ingestion frameworks leveraging Confluent Kafka for large-scale datasets.
Ensure high data quality, governance, and compliance with banking regulations while supporting data integration with core banking and CRM systems.
5 years of relevant work experience in data engineering.
Strong expertise in SQL, Databricks, Spark, PySpark, Confluent Kafka, Azure Data Factory, and Azure Data Lake Storage.
Bachelor’s or Master’s degree in a relevant field.
Experience in or knowledge of banking/financial services domain and regulatory frameworks (KYC, AML).
Experienced in designing and optimizing scalable data pipelines in cloud environments, specifically Azure ecosystem.
Comfortable operating within banking or financial services domain, understanding core banking systems and related integrations.
Skilled in implementing event-driven architectures with strong compliance and data governance focus.