Job Description: Required Skills & Experience
- 5–7 years of experience in data engineering, with at least 2–3 years of Databricks hands-on experience.
- Strong expertise in Apache Spark (PySpark/Scala/SQL) and distributed data processing.
- Solid experience with Delta Lake, Lakehouse architecture, and data modeling.
- Hands-on experience with at least one cloud platform: Azure Data Lake, AWS S3, or GCP BigQuery/Storage.
- Strong proficiency in SQL for data manipulation and performance tuning.
- Experience with ETL frameworks, workflow orchestration tools (Airflow, ADF, DBX Workflows).
- Good understanding of CI/CD, Git-based workflows, and DevOps practices.
- Exposure to MLOps and MLflow is a strong plus.
- Knowledge of data governance, cataloging, and security frameworks.
Responsibilities: Required Skills & Experience
- 5–7 years of experience in data engineering, with at least 2–3 years of Databricks hands-on experience.
- Strong expertise in Apache Spark (PySpark/Scala/SQL) and distributed data processing.
- Solid experience with Delta Lake, Lakehouse architecture, and data modeling.
- Hands-on experience with at least one cloud platform: Azure Data Lake, AWS S3, or GCP BigQuery/Storage.
- Strong proficiency in SQL for data manipulation and performance tuning.
- Experience with ETL frameworks, workflow orchestration tools (Airflow, ADF, DBX Workflows).
- Good understanding of CI/CD, Git-based workflows, and DevOps practices.
- Exposure to MLOps and MLflow is a strong plus.
Qualifications: Bachelor's/Master's in Engineering 0-2 years
