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Global brand, Bangalore metro, mid-level generalist Databricks/PySpark role increases candidate competition.
Core cloud data engineering skills (Databricks, PySpark, SQL) are highly transferable across industries.
Explicit 4–7 years plus mandatory Databricks, PySpark, SQL, and data-warehouse expertise.
Lead design and development of scalable ETL pipelines for the Enterprise Data Lakehouse using Databricks and Apache Spark.
Develop and optimize data processing solutions and implement performance optimizations including incremental data processing.
Manage source code and CI/CD pipelines using GitHub for data engineering workloads.
Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or related field.
4-7 years of relevant experience in Data Engineering, ETL, DWH, or related data analytics roles.
Strong hands-on experience with Databricks platform, PySpark, Python, SQL, Azure Data Factory, and GitHub.
Deep understanding of data warehousing concepts (dimensional modelling, Kimball/Inmon methodologies).
Experienced in building and optimizing large-scale data pipelines in regulated, high-volume environments.
Proficient with Big Data technologies such as Spark, Hadoop, and Kafka for large data processing.
Skilled at translating complex business requirements into robust ETL and data warehousing solutions.