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Tier-1 brand, generalist Data Engineer title, metro location, and broad platform skillset increase applicant competition.
Core data engineering skills are transferable, but asset-management and governance requirements add moderate industry specificity.
Senior-level Snowflake and production-grade data platform requirements make shortlisting highly selective.
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Design, build, and operate production-grade data pipelines and data models across structured and unstructured data sources using modern cloud platforms.
Deliver secure, scalable, and reusable data capabilities supporting business applications, analytics, automation, and AI use cases with governance, monitoring, and operational support.
Collaborate closely with application teams, architects, data owners, and stakeholders to ensure accessible, trusted, governed enterprise data and to modernize data delivery through reusable components and documentation.
Strong hands-on expertise with modern data engineering practices and tools, including cloud data platforms like Snowflake or similar data warehouses.
Experience with data integration patterns: API design, batch/stream processing, event-driven architectures, orchestration, and distributed systems fundamentals.
Demonstrated ability in engineering excellence: CI/CD, testing strategies, performance optimization, cost-aware design, secure-by-design delivery, and observability.
Work Experience Required: Not explicitly mentioned in the JD
Senior-level hands-on data engineer with pragmatism and discipline in production delivery and code quality.
Experienced in strategic data platform engineering with a focus on Snowflake or equivalent cloud data warehouses, including data modeling and performance tuning.
Capable collaborator who influences cross-functional teams including engineering, product, data, and compliance stakeholders to deliver governed, repeatable, and trusted data solutions.