Job Description
We are looking for a Data Engineer to join a growing team and contribute to the delivery of reliable, analytics-ready data solutions. This contract opportunity with potential for a permanent role is ideal for someone who enjoys building scalable data pipelines, improving data models, and partnering with technical and business stakeholders to support reporting, self-service analytics, and data science. The role offers the chance to work hands-on with Databricks, Python, PySpark, SQL, and modern data engineering practices in a collaborative environment focused on quality and performance.
Responsibilities:
• Build, maintain, and enhance data pipelines that support dependable data availability for analytics and reporting needs.
• Collaborate with data engineering leaders to troubleshoot defects, resolve pipeline issues, and improve overall platform stability.
• Develop transformation logic using Python, PySpark, SQL, and Databricks to prepare clean, usable datasets for downstream consumers.
• Design and refine data models that improve usability, consistency, and performance across reporting and analytical workloads.
• Apply layered data architecture principles, including Bronze, Silver, and Gold structures, to organize and manage data effectively.
• Establish and follow engineering standards for validation, testing, monitoring, and documentation to strengthen data quality and maintainability.
• Optimize processing and query performance to support efficient data delivery at scale.
• Work with business and technical partners to translate data needs into practical engineering solutions that support trusted insights.• Hands-on experience with Databricks in a data engineering environment.
• Strong programming skills in Python, including building data workflows with PySpark.
• Solid understanding of ETL development and data pipeline design.
• Proficiency in SQL for data transformation, analysis, and performance tuning.
• Experience working with medallion architecture and managing Bronze, Silver, and Gold data layers.
• Knowledge of data engineering best practices related to testing, monitoring, documentation, and long-term supportability.
• Effective communication skills with the ability to work across technical teams and business stakeholders.
• Strong business awareness and the ability to align engineering work with reporting and analytics objectives.
