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Tier-1 brand, metro location, common Data Engineer title and broad skillset increase applicant competition.
Core data engineering skills transfer across industries, though regulated-industry experience is a plus.
Mandatory 3+ years with SQL, Python, cloud and data platform experience makes screening rigorous.
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Design, develop, and maintain scalable data ingestion, transformation, and integration pipelines supporting analytics, reporting, and AI/ML solutions.
Build and optimize ETL/ELT workflows processing large, structured and unstructured datasets across cloud-based data platforms and warehouse/lakehouse environments.
Collaborate with stakeholders across business and technology to deliver reliable data solutions, troubleshoot production issues, and promote engineering best practices including automation and CI/CD.
Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, or related field.
3+ years of experience in data engineering, software engineering, or big data development.
Proficiency in SQL and Python, experience developing ETL/ELT pipelines and working with large-scale datasets.
Experience with cloud-based data platforms (Azure, AWS, or Google Cloud) and understanding of data modeling, data quality, and governance principles.
Experienced with modern cloud data technologies such as Databricks, Spark/PySpark, Snowflake, or BigQuery and workflow orchestration tools like Airflow, Azure Data Factory, or Cloud Composer.
Demonstrates ability to independently handle moderately complex data engineering challenges and collaborate effectively with diverse technical and business stakeholders.
Preferably has worked in regulated industries like healthcare or financial services, indicating familiarity with compliance and governance requirements in data engineering.