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Medium — niche MLOps skills but metro locations and moderate employer brand produce moderate competition.
Medium — MLOps skills transfer across industries but require specific Databricks and ML lifecycle experience.
High — explicit 8+ years plus many mandatory platform, tooling, and CI/CD requirements.
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Develop and maintain robust data pipelines for ML model development using Python, SQL, PySpark, and cloud platforms.
Lead design and implementation using Databricks platform including Delta Lake and workflow management.
Build and maintain shared tools and libraries (e.g., data engineering utilities, data quality libraries) used across teams to standardize processes.
Bachelor’s or master’s degree in Computer Science, Data Science, Engineering, or related field.
8+ years professional experience including 4+ years with Python, SQL, PySpark, and bash scripting.
3+ years experience with Cloud Data Warehousing platforms (Redshift, Snowflake, Databricks SQL) and distributed frameworks like Spark.
2+ years hands-on experience with Databricks platform, and working knowledge of CI/CD tools, version control, and orchestration tools.
Strong expertise in software development lifecycle, engineering best practices, and API development especially in Python frameworks (Flask / FastAPI).
Experienced in containerization (Docker/Kubernetes) and building shared reusable libraries across multiple teams.
Proven ability to collaborate effectively with cross-functional technology and business teams to deliver scalable MLOps solutions.