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Medium: mid-level popular ML role, cross-cloud and generalist skills, non-Tier‑1 employer.
Medium: core ML and data engineering skills are transferable, but GIS/OCR domain specifics increase specialization.
High: explicit 5–8 years and many mandatory cloud, ML, and tooling requirements.
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Lead the design and implementation of scalable data pipelines and ETL processes using Apache Airflow, PySpark, and Databricks.
Architect, deploy, and monitor data engineering and statistical solutions on cloud platforms including Azure, AWS, and GCP using relevant services.
Collaborate cross-functionally with domain experts and mentor junior staff while contributing to strategic initiatives.
5–8 years of experience in data science, automation, or data engineering.
Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, or related field.
Advanced proficiency in Python, Git, and cloud platforms (Azure, AWS, GCP).
Strong expertise in NLP, OCR, machine learning, image processing, and data pipeline architecture.
Experienced technical consultant comfortable leading multi-cloud deployment projects and data engineering at scale.
Proven ability to combine software engineering (version control, reusable code) with advanced data science techniques (NLP, OCR, image processing).
Operates effectively in cross-functional teams with leadership and mentorship capabilities.