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Strong employer brand, generalist mid-level role, and metro location increase candidate competition.
Data engineering skills broadly transferable, though vector/financial data expertise increases domain specificity.
Explicit 3+ years plus mandatory cloud, SQL, vector-database and language skills create high filtering.
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Lead design, implementation, and optimization of data pipelines for vectorized data storage and retrieval on cloud platforms like Snowflake and Databricks.
Develop and maintain high-performance ETL/ELT processes handling large-scale analytical and AI/ML workloads, ensuring system reliability and scalability.
Collaborate cross-functionally to define data architecture and mentor team members, contributing to modernization from Perl to Python and leveraging cloud native technologies.
Minimum 3 years experience in system design for large-scale, distributed systems.
Proficiency in Go, Python, SQL, and familiarity with cloud platforms AWS, Azure, Snowflake, Databricks, or similar.
Hands-on experience with vector data storage, vector databases, similarity search technologies, and batch or real-time data processing.
Work Experience Required: At least 3+ years in relevant system and data engineering roles.
Experienced in building scalable, cloud-native data platforms optimized for vector data and AI/ML integration.
Strong background in data engineering and backend development with familiarity in modern DevOps practices and CI/CD pipelines.
Comfortable operating in a multi-location cross-functional team environment, leading technical initiatives and mentoring junior engineers.