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Mid-level data engineer in a metro city with broad Databricks/Spark skills increases applicant competition.
Core data engineering skills are transferable, though banking governance and platform specifics raise domain sensitivity.
Explicit 4+ years preference plus mandatory Databricks, pySpark, Hadoop and CI/CD skills make filters strict.
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Design, develop, and optimize high-performance data pipelines using Databricks and pySpark within Hadoop ecosystems.
Own data architecture decisions including storage, processing, security, and data governance implementation.
Ensure system stability and performance through monitoring, troubleshooting, and applying CI/CD and testing best practices.
Bachelor's degree in Computer Science, Information Technology, or equivalent.
Strong hands-on experience with pySpark and Databricks for distributed data processing.
Proven experience with Hadoop ecosystem components (Hive, HDFS, YARN, Oozie, Starburst) and building large-scale data pipelines.
Work Experience Required: Preferred 4+ years in data engineering or large-scale distributed systems.
Experienced in managing complex Big Data environments with strong competencies in performance tuning and data modeling.
Familiar with CI/CD pipelines, Git, Agile methodologies, and cross-cultural collaboration in global teams.
Knowledgeable in data governance, security standards, and cloud platforms such as Azure or AWS aligning with Databricks usage.