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Remote role, popular data engineer title, and broad tech requirements increase applicant competition.
Core data engineering skills transfer across industries, but vector-db and AI pipelines increase domain specificity.
Explicit 7+ years and majority-data-engineering requirement plus specific tech needs enforce strict shortlisting.
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Architect and build scalable data pipelines and infrastructure supporting AI and operational product systems including real-time and batch data workflows.
Lead the data architecture, tooling, engineering standards, culture, and hiring bar as the founding senior data hire in a growing data team.
Collaborate with AI engineers, backend teams, and product leadership to ensure reliable processing of large-scale operational data and influence long-term data strategy.
7+ years professional experience with majority in dedicated data engineering roles.
Strong expertise in designing and building data pipelines and distributed data systems with relational (PostgreSQL preferred) and NoSQL databases.
Proficiency in Python programming and demonstrated ability to make architectural decisions.
Experience with vector databases used in modern AI systems and scalable backend system design.
Experienced in building and optimizing AI platform data infrastructures including vector search and machine learning pipelines.
Comfortable operating as a senior technical leader shaping data engineering culture and standards in an early-stage or startup environment.
Familiar with data frameworks such as Apache Spark, Airflow, Kafka, and database technologies like Qdrant, Milvus, PostgreSQL, and skilled in Python data-processing libraries.