Data Engineer
Chicago, IL
Full-time
Hybrid
1 month ago
About the role
Role: Data Engineer
Location: Chicago, IL
Work Model: Hybrid 3 Days Onsite
Interview Process: In-Person Final Round
Mandatory Skills: Databricks, SQL
Role
Based on the discussion and hands-on assessment, the candidate currently does not meet the expectations for this role. Please find below the observations:
Databricks: The candidate's experience with Databricks appears to be recent and primarily limited to notebook-level usage. There were challenges in confidently answering standard Databricks concepts and explaining implementation details.
Data Engineering Concepts: Limited understanding of data refresh cycles, job orchestration, and daily pipeline planning, which are critical requirements for this position.
Data Warehousing & Reporting: Hands-on exposure to data warehousing concepts, ETL processes, and reporting/data preparation appears to be minimal.
AI/ML Exposure: Although AI/ML experience was initially mentioned, it was clarified during the discussion that the exposure is limited to consuming outputs rather than hands-on development or implementation.
Hands-on Exercise: The practical exercise was not completed as expected, indicating gaps in problem-solving ability and practical application of core concepts.
Location: Chicago, IL
Work Model: Hybrid 3 Days Onsite
Interview Process: In-Person Final Round
Mandatory Skills: Databricks, SQL
Role
Based on the discussion and hands-on assessment, the candidate currently does not meet the expectations for this role. Please find below the observations:
Databricks: The candidate's experience with Databricks appears to be recent and primarily limited to notebook-level usage. There were challenges in confidently answering standard Databricks concepts and explaining implementation details.
Data Engineering Concepts: Limited understanding of data refresh cycles, job orchestration, and daily pipeline planning, which are critical requirements for this position.
Data Warehousing & Reporting: Hands-on exposure to data warehousing concepts, ETL processes, and reporting/data preparation appears to be minimal.
AI/ML Exposure: Although AI/ML experience was initially mentioned, it was clarified during the discussion that the exposure is limited to consuming outputs rather than hands-on development or implementation.
Hands-on Exercise: The practical exercise was not completed as expected, indicating gaps in problem-solving ability and practical application of core concepts.
Skills
Medical Equipment ManufacturingInformation TechnologyIT Services and IT Consulting