Back
Mid-level, popular data platform role but non-tier company and multi-database specialization moderates applicant density.
Data engineering and backend platform skills transfer well across industries, though AI integration favors analytics-focused firms.
Explicit 5-7 years and strong mandatory data engineering, database, and backend platform experience.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and develop scalable back-end systems and data ingestion/transformation pipelines supporting AI/ML workflows.
Define and implement robust data architecture, including unified data models and multi-database integration (relational, NoSQL, graph).
Collaborate closely with AI/ML teams to prepare and integrate clean, structured data for model training and consumption, ensuring system scalability and performance.
Bachelor's degree in Computer Science, IT, Software Engineering, Data Science or related field; Master's preferred.
5 - 7 years of relevant experience in data engineering and back-end system design.
Strong hands-on experience with relational databases (PostgreSQL, MS SQL Server, MySQL) and working knowledge of NoSQL and graph databases.
Proven expertise in data engineering concepts, scalable system architecture, and API design (e.g., FastAPI, Flask, Django).
Experienced in multi-database environments integrating relational, time-series, and graph databases to support scalable AI data platforms.
Ability to design and manage end-to-end data flow and integration strategies in exploratory, fast-iteration development settings.
Comfortable collaborating with AI/ML teams to build data pipelines aligned with model training and consumption requirements.