Databricks Engineer

The Databricks Engineer will design, develop, and optimize scalable data engineering solutions using the Databricks Lakehouse Platform. This role focuses on building robust data pipelines, processing large datasets, implementing data transformation workflows, and supporting enterprise analytics initiatives., * Develop and maintain scalable data pipelines using Databricks and PySpark.

• Build ETL/ELT workflows to process structured and unstructured data.
• Implement data transformation and validation using Spark SQL and Python.
• Design and manage Delta Lake tables and optimize data storage.
• Configure and maintain Databricks clusters, jobs, and workflows.
• Integrate data from multiple sources into centralized lakehouse environments.
• Optimize Spark workloads, query execution, and cluster performance.
• Implement data quality checks, monitoring, and error-handling mechanisms.
• Apply Unity Catalog and governance standards to secure enterprise data.
• Collaborate with data architects, analysts, and business teams to deliver analytics solutions.

• Strong hands-on experience with Databricks and the Lakehouse architecture.
• Proficiency in Python, PySpark, and SQL for large-scale data processing.
• Experience with Apache Spark, Spark SQL, and Delta Lake.
• Knowledge of data ingestion, ETL/ELT pipelines, and data transformation frameworks.
• Experience with Azure Databricks, AWS Databricks, or Databricks on Google Cloud.
• Familiarity with cloud storage services such as ADLS, Amazon S3, and Google Cloud Storage.
• Experience with Delta Live Tables, Workflows, Unity Catalog, and Databricks SQL.
• Knowledge of data modeling, dimensional modeling, and data warehousing concepts.
• Familiarity with orchestration tools such as Apache Airflow, Azure Data Factory, or Databricks Workflows.
• Experience with Git, CI/CD pipelines, and automated deployment practices.
• Understanding of data governance, access controls, security, and performance optimization., * 5+ years of experience in data engineering or big data development.
• 3+ years of hands-on experience with Databricks and Apache Spark.
• Strong programming experience with Python, PySpark, and SQL.
• Experience developing production-grade data pipelines in cloud environments.
• Understanding of distributed computing, data architecture, and performance tuning.
• Strong analytical, debugging, and problem-solving skills., * Bachelor’s degree in Computer Science, Information Technology, Data Engineering, or a related field.
• Master’s degree is preferred.
• Databricks certifications such as Data Engineer Associate or Data Engineer Professional are an advantage.