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Tier-1 brand, generic software title, mid-level experience, metro location, and broad tech stack increase competition.
Highly domain-specific vector databases, Snowflake/Databricks and cloud-data platform expertise reduces cross-industry transferability.
Explicit 3+ years plus mandatory Snowflake/Databricks, vector-data and multi-language tech requirements create strict filters.
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Design, implement, and optimize scalable data pipelines for vectorized data across cloud platforms like Snowflake and Databricks.
Lead and mentor software/data engineers; collaborate with cross-functional teams (data science, analytics, product) to define data requirements and architecture for vector data workflows.
Modernize and maintain data delivery systems focusing on speed, system design, reliability, and performance across large-scale datasets and AI/ML workloads.
Bachelor's or Master's degree in Computer Science, Engineering, or related field.
Minimum 3 years experience in system design for large-scale, distributed systems.
Strong proficiency in Python, C#, Go, AWS, Snowflake, Databricks and/or cloud-native application development on AWS/Azure/GCP.
Hands-on experience with vector data storage, vector databases, similarity search, and modern datalake/cloud data platforms like Snowflake, Databricks, Redshift.
Experienced in combining backend development with data engineering for enterprise cloud platforms, focusing on large-scale analytical and AI/ML workflows.
Capability to lead technical architecture decisions and mentor engineering teams on software craftsmanship and data engineering best practices.
Track record working with vectorized data solutions and integrating cloud data platforms with AI/ML and LLM applications in cross-functional settings.