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Popular data role, metro location, and broad Azure/Spark skillset increase applicant competition.
Core data engineering skills (Spark, SQL, ETL) are transferable across industries.
Explicit 7+ years plus many mandatory Azure, Spark, SQL and data-engineering must-haves.
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Design, develop, and maintain scalable, secure enterprise-level data pipelines for real-time and batch processing using big data technologies.
Collaborate with cross-functional teams to translate business requirements into data solutions and operationalize machine learning models.
Ensure data quality, governance, and operational excellence while mentoring junior engineers and contributing to documentation and peer code reviews.
7+ years of overall experience in data engineering or related fields.
5+ years experience building data pipelines for structured and unstructured data; 2+ years with big data technologies such as PySpark, Hadoop, Kafka, EventHub, Stream Analytics.
3+ years experience with relational and dimensional data modeling using SQL Server and data warehousing, including T-SQL and performance tuning.
Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
Experienced with Azure data services including Azure Data Factory, Azure Data Lake, Azure SQL DB, and Azure Synapse Analytics (3+ years).
Skilled in working with business stakeholders for requirements gathering and use case analysis, indicating strong cross-team collaboration.
Familiar with Agile/Scrum methodologies, DevOps practices, and CI/CD pipelines; Azure Data Engineer certification is a strong advantage.