Back
Medium due to common Data Engineer title, required Snowflake certification, and metro/early-mid candidate pool.
Medium because core data engineering skills transfer, but Snowflake certification and top-tier degree restrict some candidates.
High because mandatory SnowPro certification, top-tier degree requirement, and explicit PySpark experience filter candidates.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build, and maintain scalable data pipelines for analytics, reporting, and AI/ML use cases.
Lead migration of legacy data architectures to cloud-based, AI-ready data platforms with semantic layers for business-friendly data access.
Collaborate with business stakeholders, data scientists, and product teams to translate data requirements into technical solutions ensuring data quality and governance.
Bachelor’s or Master’s degree in Economics, Statistics, Engineering, or related quantitative field from a top-tier university.
Minimum 2+ years hands-on experience in advanced Python & PySpark, including performance tuning and complex query optimization.
Hands-on experience with data platforms such as Snowflake, Databricks, BigQuery, or Redshift.
Snowflake certification required: SnowPro Core mandatory; SnowPro Advanced preferred.
Experienced in modern cloud data architectures (e.g., data lakes, lakehouses, data warehouses) and migrations of legacy systems.
Proven ability to collaborate cross-functionally with technical and non-technical teams to implement data solutions supporting AI/ML and BI.
Technical proficiency with Python/PySpark and Snowflake certification indicates readiness to manage and optimize complex data pipelines at scale.