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Tier-1 brand, manager-level generalist role, broad tech list, and metro hiring increase competition.
Core data engineering skills transfer across industries, but financial-document and metadata expertise raise specificity.
Explicit 8+ years, specific data engineering tech stack, and domain expertise make filtering strict.
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Lead a Data Engineering team of 5-8 engineers building automated, scalable data pipelines and platforms for financial data processing.
Own architecture, design, and implementation of cloud-native, event-driven, AI-assisted data platforms and document processing systems.
Drive engineering execution, coaching, and cross-team collaboration to deliver scalable solutions automating large-volume data workflows.
8+ years of software engineering experience with strong expertise in data engineering.
Proven experience managing engineering teams in a product-focused organization.
Strong technical skills including Python, Pandas, NumPy, PyArrow, FastAPI, PostgreSQL, DynamoDB, Redis, Elasticsearch, AWS S3, Docker.
Bachelor's degree in Engineering, Computer Science, or equivalent.
Experienced leader balancing hands-on technical depth with people and execution management in scalable data pipeline environments.
Technical expertise in cloud-native AWS architectures, automation-first engineering practices, and AI-assisted data platforms.
Background or familiarity with financial data engineering and document processing at scale, comfortable navigating cross-functional stakeholder environments.