Seeking a skilled Databricks Developer to architect, build, and maintain robust, scalable data pipelines and advanced analytics solutions. This role is crucial for driving enterprise data integration and analytics initiatives, requiring a deep understanding of Apache Spark, Python, and SQL within cloud environments.

This position focuses on leveraging Databricks for efficient data processing and transformation. Candidates will contribute to building modern data architectures that support complex analytical needs and business intelligence.

Key duties include designing and implementing ETL/ELT pipelines using Databricks, and developing scalable data processing frameworks with Apache Spark (PySpark). You will build and optimize both batch and streaming data pipelines, integrating data from diverse sources like databases, APIs, and cloud storage.

Responsibilities also involve implementing data transformation, cleansing, and validation. You'll develop and optimize Delta Lake tables, create reusable Databricks notebooks and workflows, and troubleshoot pipeline performance. Collaboration with stakeholders, adherence to coding standards, and participation in code reviews are essential.

Essential qualifications include strong experience with Databricks, Apache Spark/PySpark, Python proficiency, and solid SQL skills. Proven experience in ETL/ELT development and working with Delta Lake is required. Familiarity with data warehousing concepts and Git for version control is also mandatory.

Preferred skills encompass experience with cloud platforms like Azure (Azure Databricks, ADLS Gen2, Azure Data Factory), AWS (S3, Glue, EMR), and Google Cloud (BigQuery, Cloud Storage). Knowledge of streaming technologies, orchestration tools like Airflow, and data governance concepts is advantageous.