Big data/Python/Databricks Engineer Engineer
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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, mid-level data engineering title, and broad required skillset make competition high.
Core data engineering skills are transferable, but finance governance and compliance preferences increase domain sensitivity.
Explicit 5–8 years plus mandatory Python/PySpark, Hadoop, Databricks, SQL, and LLM expertise narrows shortlist.
Job Description
Structured overview of role & requirementsAbout This Role
Build and maintain scalable data pipelines using Python and PySpark to process large volumes of structured and unstructured data across distributed platforms.
Develop and optimize data workflows on Hadoop-based ecosystems (HDFS, Hive) ensuring reliable data availability for reporting and analytics.
Manage full data application lifecycle including design, testing, implementation, and production support, collaborating with analytics teams for business insights.
Minimum Requirements
5 to 8 years of experience in data engineering or big data development with large-scale data platforms.
Proficient in Python, PySpark, ETL, Oracle DB, SQL, and Databricks.
Practical knowledge of Hadoop ecosystem including HDFS, Hive, Hadoop cluster operations, and Linux environments.
Bachelor's degree or equivalent in a relevant technical discipline.
Ideal Candidate Profile
Experienced in handling enterprise-scale big data tools and modern data platforms with ability to work autonomously and make technical decisions.
Familiar with managing data engineering projects including risk assessment, security, and compliance alignment.
Skilled in supporting Tableau dashboards and leveraging statistical/data exploration tools like RStudio and Large Language Models (LLMs).