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Popular mid-level Data Engineer with common Databricks/Spark skills, leading to high applicant competition.
Data engineering skills (Spark, Databricks, cloud) are widely transferable across industries.
Mandatory 5+ years plus Databricks, Spark, Delta Lake and cloud expertise enforces strict screening.
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Design, develop, and optimize large-scale data pipelines using Databricks, Apache Spark, and Delta Lake.
Ensure data quality, reliability, performance, and scalability of data ingestion, transformation, and integration workflows.
Collaborate with data architects and analysts to support analytics and reporting; work within Agile framework for delivery.
5+ years of data engineering experience, with strong focus on Databricks.
Proficiency in Apache Spark, Delta Lake, Databricks Lakehouse architecture, and strong SQL (joins, window functions, optimization).
Experience with Python, PySpark, and cloud platforms (Azure preferred, also AWS/GCP).
Familiarity with ETL/ELT processes, CDC, SCD Types 1 & 2, and CI/CD/DevOps practices.
Experienced in Lakehouse architecture and cloud-native data engineering environments.
Skilled at monitoring, troubleshooting, and performance tuning of data pipelines at scale.
Able to collaborate cross-functionally with data architects and analysts within Agile delivery teams.