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Tier-1 brand and metro locations raise competition, though 10+ years seniority moderates applicant density.
Data engineering skills (ETL, cloud, warehousing) are fairly transferable across industries.
Explicit 10+ years requirement and multiple mandatory data platform skills.
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Design, develop, and maintain scalable data pipelines and analytics solutions across enterprise and cloud platforms.
Translate business and architecture requirements into technical specifications, scalable data models, and production-ready code.
Collaborate with cross-functional teams to deliver incremental, testable solutions and support production including testing, deployment, and issue resolution.
Bachelor's degree in computer science, software engineering, information systems, or a related field.
10+ years of experience building data engineering solutions using ETL/ELT tools such as Azure Data Factory, Alteryx, or cloud-native integrations.
10+ years of experience with data warehousing or data lake platforms (e.g., SAP HANA, Snowflake, Azure Data Lake Storage, Amazon Redshift, Google BigQuery).
10+ years of experience building cloud-based engineering solutions on Microsoft Azure, AWS, or Google Cloud Platform.
Experienced with modern engineering practices including Agile, DevSecOps, and Site Reliability Engineering.
Skilled at collaborating across product, engineering, and cross-functional stakeholders for delivery aligned to business goals.
Has a strong background in creating technical specifications and developing maintainable, scalable, supportable code for data and analytics solutions.