Back
Mid-level, popular Data Engineer title with 5+ years and broad skill demands increases applicant density.
ETL, Databricks, and SQL skills are broadly transferable, but KPI domain knowledge adds moderate specificity.
Mandatory 5+ years plus specific Databricks, PySpark, SQL and ML/MLOps requirements enforce strict filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own the design, development, and maintenance of scalable data pipelines using Databricks and PySpark to ensure high data quality and enable efficient analysis.
Collaborate with business stakeholders to translate KPIs into data requirements, perform advanced analytical and statistical analysis, develop AI/ML models, and deliver actionable insights through visualizations and dashboards.
Lead deployment and monitoring of machine learning models using MLOps best practices, ensuring model performance and scalability for business optimization.
5+ years of hands-on experience in data analytics or business intelligence roles.
Proficiency in SQL including complex queries and joins.
Experience with Databricks or other modern data platforms like Snowflake, BigQuery, or Redshift.
Working knowledge of Python for data manipulation and visualization.
Experienced in end-to-end data engineering and advanced analytics including statistical modeling and machine learning deployment in production.
Strong business orientation with ability to translate complex KPIs into actionable data insights and communicate effectively with cross-functional teams.
Comfortable working in a mid-size, fast-growing tech environment involving advanced AI, IoT, and new technology projects.