Back
Mid-level role, metro location, and popular Databricks/Spark skillset create high applicant competition.
Data engineering skills are transferable across industries but require pipeline and platform-specific experience.
Explicit 4–6 years plus mandatory Databricks, PySpark, Python, and SQL requirements enforce strict filtering.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, and maintain scalable ETL/ELT pipelines using Databricks, PySpark, and Python for large-scale data processing.
Write complex SQL queries for data extraction, transformation, validation, and reporting, and optimize Spark jobs and workflows for performance and scalability.
Collaborate with data analysts, stakeholders, and development teams to understand requirements, perform data quality checks, troubleshoot data issues, and participate in code reviews and production support.
3+ years of experience in Data Engineering with hands-on experience in Databricks, PySpark, Apache Spark, Python, and ETL/ELT pipelines.
Proficiency in SQL, including complex queries, joins, window functions, and performance tuning.
Bachelor's degree in Computer Science or a related field (minimum qualification).
Work Experience Required: 4-6 years as per JD; Notice Period: Not explicitly mentioned in the JD.
Experienced data engineer proficient in Databricks, Delta Lake, PySpark, and building scalable data pipelines in an enterprise environment.
Skilled in optimizing complex Spark jobs and SQL queries with a strong focus on data quality and performance.
Experienced collaborator able to engage with cross-functional teams including analysts and stakeholders to deliver data solutions aligned with business needs.