Seeking a motivated Data Engineer with 3-5 years of experience to design, develop, and optimize modern data platforms. This role involves building scalable data pipelines, implementing Data Vault modeling, and supporting enterprise data warehousing initiatives using Databricks, PySpark, SQL, and Azure services.

This is a full-time position requiring a strong understanding of data engineering best practices and a commitment to delivering high-quality data solutions. The ideal candidate will possess excellent analytical and problem-solving skills, with the ability to collaborate effectively with stakeholders in a fast-paced environment.

Key responsibilities include designing and maintaining scalable data pipelines with PySpark and Databricks, implementing Data Vault 2.0 models (Hubs, Links, Satellites), and developing ETL/ELT processes for diverse data types.

You will also build and optimize complex SQL queries, leverage Azure Data Services (Data Factory, Data Lake Storage, Synapse, Databricks), and ensure data quality, security, and governance. Monitoring performance, supporting CI/CD, and troubleshooting issues are integral parts of this role.

Requires 3-5 years of hands-on Data Engineering experience, with strong expertise in Databricks, PySpark, and Data Vault 2.0 modeling. Advanced SQL skills and performance tuning are essential.

Proficiency in Azure Cloud services such as Azure Databricks, Azure Data Factory, Azure Data Lake Storage, and Azure Synapse Analytics is mandatory. Familiarity with ETL/ELT frameworks, large-scale data processing, data warehousing concepts, Git, CI/CD, and Agile methodologies is expected. A Bachelor's or Master's degree in a related technical field is required.