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Tier-1 employer plus mid-level, popular Data Engineer title and metro location increases candidate competition.
Core data engineering skills (PySpark, Databricks, SQL) transfer across industries, so background sensitivity is low.
Explicit 2-6 year requirement plus mandatory Databricks, PySpark, Python and Delta Lake skills raises filter strictness.
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Develop, test, and maintain scalable ETL/ELT data pipelines using Databricks, PySpark, and Python for analytics, reporting, and ML use cases.
Perform data ingestion, transformation, cleansing, validation, and quality checks on structured and semi-structured data from multiple sources.
Optimize Spark jobs and monitor scheduled workflows to ensure performance, reliability, and timely data delivery.
2-6 years of relevant experience with a Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, Mathematics, or related field, or equivalent practical experience.
Hands-on experience with Python, PySpark, and Databricks including notebooks, clusters, jobs, workflows, and Delta Lake.
Strong knowledge of ETL/ELT processes, SQL querying, data pipelines, and working with structured and semi-structured data formats (CSV, JSON, Parquet, Delta).
Familiarity with cloud platforms (AWS, Azure, or GCP), version control (Git), and basic understanding of AI/ML data preparation.
Experienced in building and troubleshooting production-grade data pipelines and optimizing Databricks/Spark workloads for efficiency and cost.
Comfortable collaborating with cross-functional teams including data engineers, analysts, and data scientists in Agile environments.
Knowledgeable in data lakehouse architectures, Delta Lake features, and supporting AI/ML workflows via data preparation and feature engineering.