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High due to common Data Engineer title, mid-level experience band, metro location, and broad skillset.
Medium because core data engineering skills transfer across industries but Azure/Databricks specialization raises domain specificity.
Medium because explicit 2–4 years plus mandatory Azure Databricks, PySpark, and SQL skills.
Develop and maintain reliable, scalable ETL/ELT data pipelines and data storage solutions using Azure Databricks, PySpark, Python/Scala, and SQL.
Design, implement, and optimize Data Lake and Lakehouse architectures on Azure services including ADLS, Event Hubs, and Azure Functions.
Ensure data quality, integrity, governance, and support continuous improvement within Agile environments applying DevOps and Scrum/Kanban practices.
Bachelor's degree or equivalent in Computer Science, IT, Engineering, Data Science, or related technical field, or equivalent experience.
2–4 years of relevant Data Engineering experience preferred; early-career candidates with relevant academic/technical experience may be considered.
Strong programming skills in Python/PySpark or Scala, plus SQL proficiency; hands-on experience with Azure Databricks and Azure data services (ADLS, Event Hub, Azure Functions).
Work Experience Required: 2–4 years of relevant Data Engineering experience preferred
Experienced in designing and operating large-scale cloud-based data pipelines and storage (Data Lake/Lakehouse) on Azure platforms.
Proficient in Agile methodologies (Scrum, Kanban) and DevOps for continuous delivery and improvement of data solutions.
Skilled in implementing data quality, governance, and troubleshooting mechanisms to ensure reliable and maintainable data infrastructure.