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Metro location, common data-engineer skillset, broad requirements, and recognizable employer increase candidate competition.
Medium—core data engineering skills are transferable, but sector references (banking, manufacturing) add domain bias.
Moderate strictness due to required data engineering tools and senior title but no explicit years listed.
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Design and develop scalable data processing systems and maintain data pipelines for large data volumes.
Optimize data infrastructure for performance and reliability, and troubleshoot data issues promptly.
Collaborate with data scientists and analysts; mentor junior data engineers on best practices and technical skills.
Proficiency in Snowflake, DBT, SQL, and Databricks Platform.
Bachelor’s degree in Business Analytics, Computer Science, Statistics, or Master’s in Data Science.
Certifications like Azure Data Engineer Associate, Databricks Certified Data Engineer, or equivalent preferred.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced with cloud computing and big data technologies to support scalable AI and data solutions.
Comfortable operating in hybrid and agile environments with strong collaboration across teams.
Ability to lead technical mentorship and implement data engineering best practices.