Big data_Python_Databricks Engineer Engineer
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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, hybrid mid-level data engineer role in metros creates high applicant competition.
Core data engineering skills transfer across industries, though banking governance adds moderate domain specificity.
Explicit 5–8 years plus mandatory Python, PySpark, Hadoop, Databricks, SQL, and Tableau increases selection rigidity.
Job Description
Structured overview of role & requirementsAbout This Role
Design, build, and maintain scalable data pipelines and workflows using Python, PySpark, and Hadoop ecosystem components (HDFS, Hive) to support large-scale data reporting and analytics.
Manage full data application development lifecycle including analysis, design, testing, implementation, and production support in distributed computing environments.
Collaborate with analytics teams to build Tableau dashboards and ensure data processes operate reliably within Linux environments, complying with security and governance standards.
Minimum Requirements
5 to 8 years of experience in data engineering, big data development, or software application development focused on large-scale data platforms.
Proficiency in Python, PySpark, ETL, Oracle DB, SQL, and practical knowledge of Hadoop (HDFS, Hive) and Databricks environments.
Working experience with Linux environments including scripting, job scheduling, and process management.
Bachelor's degree or equivalent experience in a relevant technical discipline.
Ideal Candidate Profile
Experienced in end-to-end data pipeline design and optimization for enterprise-scale financial data environments.
Capable of managing technical planning, risk assessment, and adherence to data governance within complex distributed data ecosystems.
Able to independently operate with limited supervision, acting as a subject matter expert in big data and analytics platforms, including familiarity with LLMs and cloud-native data platforms.