Databricks Data Engineer L Contract

Job Responsibilities Design, develop, and maintain scalable data solutions to improve data accessibility, usability, and reliability across the organization. Build, optimize, and support end-to-end data pipelines and ETL/ELT processes for data ingestion, transformation, and integration. Collaborate with business stakeholders and cross-functional teams to understand data requirements and deliver effective data-driven solutions. Query, cleanse, transform, and validate large datasets using SQL, Python, Databricks, and PySpark. Monitor, troubleshoot, and optimize data pipelines to ensure high performance, scalability, and operational reliability. Conduct regular data quality assessments and implement controls to ensure data accuracy, consistency, and integrity. Ensure compliance with data governance standards and relevant government requirements, including IM8 standards where applicable. Develop, enhance, and maintain dashboards and reports for operational monitoring, performance tracking, and user behavior analytics. Translate business requirements into meaningful visualizations and actionable insights using tools such as Power BI and Tableau. Create and maintain comprehensive technical documentation, including data workflows, process documentation, dashboard user guides, and data dictionaries. Maintain and update knowledge repositories (e.g., Confluence) to support knowledge sharing and operational continuity. Stay current with emerging technologies, industry trends, and best practices in data engineering and analytics, driving continuous process improvements and innovation. Job Qualification Bachelor's Degree in Computer Science, Information Systems, Data Engineering, Data Science, or a related discipline. Minimum 5 years of hands-on experience in Data Engineering, including data pipeline development, ETL/ELT processes, and data integration projects. Proficiency in SQL and Python for data processing, transformation, automation, and analytics. Hands-on experience with Databricks, PySpark, or similar big data and analytics platforms. Experience designing, developing, and maintaining scalable and reliable data pipelines. Proficiency in dashboard development and data visualization tools such as Power BI, Tableau, or equivalent platforms. Good understanding of data quality management, data governance, and data validation practices. Experience producing high-quality technical documentation, user guides, and process documentation. Familiarity with system testing methodologies, including SIT, UAT, and QA testing. Knowledge of Apache Spark and distributed data processing frameworks. Working knowledge of Git or other version control systems. Demonstrated ability to work independently while collaborating effectively within cross-functional teams. Highly organized, detail-oriented, and committed to delivering high-quality solutions in a fast-paced environment.