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Specialized senior MLOps role in metro with moderate employer brand yields medium applicant density.
Core MLOps platform skills are transferable, but senior Databricks and enterprise ML experience create moderate domain specificity.
Many mandatory senior technical requirements (8+ years, Databricks, Delta Lake, Spark, CI/CD, Docker/K8s) make filters highly strict.
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Lead development and maintenance of robust data pipelines for structured and unstructured data supporting ML model building.
Manage data engineering work on Databricks platform including Delta Lake, workflows, and job clusters.
Develop and maintain shared tools, libraries, and APIs used by multiple teams across the organization.
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.
8+ years of relevant 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 systems like Spark.
2+ years hands-on experience with Databricks platform, including Delta Lake and related toolsets.
Experienced in software development lifecycle and engineering best practices applicable to data and ML engineering.
Proficient in containerization (Docker/Kubernetes), CI/CD tools (Jenkins or equivalent), version control, and workflow orchestration tools (Airflow, Prefect or equivalent).
Hands-on with machine learning lifecycle understanding, REST API development (Flask/FastAPI), and building reusable engineering tools across multiple teams.