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Tier-1 brand, mid-level generalist data role and broad cloud/stack requirements increase competition.
Core data engineering skills are transferable, but finance portfolio domain expectations raise fit sensitivity to medium.
Mandatory 5+ years and specific Snowflake/Python/SQL production data engineering expertise create strict filters.
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Lead design, development, and optimization of scalable data pipelines and platforms for structured and unstructured data in support of analytics and AI use cases.
Own end-to-end data engineering solutions: ingestion, transformation, orchestration, storage, serving, and data quality controls across cloud environments.
Provide technical leadership including architecture, mentoring junior engineers, code reviews, and cross-team collaboration to deliver enterprise-grade data products.
Bachelor's or Master's degree in Computer Science/Engineering or related field.
5+ years of relevant experience in data engineering.
Strong skills in Python, SQL, cloud platforms (e.g. Snowflake), data modeling, ETL/ELT design, and pipeline optimization.
Experience with data quality, monitoring, lineage practices, APIs, distributed systems, Git, CI/CD, and software engineering best practices.
Experienced technical leader capable of owning complex data initiatives and making architectural decisions.
Deep expertise in scalable cloud-based data engineering solutions supporting analytics and AI platforms.
Effective collaborator with multidisciplinary teams (AI engineers, product managers, business stakeholders) transforming requirements into production-grade data products.