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Strong Tier-1 brand and metro hiring increase competition, though senior specialized data-engineering narrows the pool.
Core data engineering skills transfer across industries, but financial-domain and document-processing experience increases specificity.
Explicit 8+ years, mandatory data-engineering skills, leadership and specific tech stack create high shortlisting strictness.
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Lead a team of 5-8 engineers to build and operate scalable, automated data engineering platforms processing large volumes of financial data and documents.
Own architecture, design, and implementation decisions for high-performance, cloud-native data pipelines leveraging AWS services and containerized deployments.
Collaborate with Product and Architecture leadership to translate complex business challenges into scalable technical solutions, ensuring execution excellence and engineering practice improvements.
8+ years of software engineering experience with strong focus on data engineering and backend systems.
Proven experience leading engineering teams in a product organization environment.
Proficiency in Python and related data engineering technologies including Pandas, NumPy, PyArrow, FastAPI, PostgreSQL, DynamoDB, Redis, Elasticsearch, Amazon S3, Docker, and AWS cloud-native development.
Bachelor's degree in Engineering, Computer Science, or equivalent.
Experienced in building scalable, automated data pipelines and document processing platforms within financial data engineering domains.
Strong system design and architecture skills with ability to make technical decisions balancing depth and execution.
Comfortable leading cross-functional collaboration, coaching engineers, and driving an ownership-driven engineering culture focused on automation and operational excellence.