Fullstack

Job summary

Job Summary This hybrid role is for an experienced developer with strong skills in Python Databricks SQL Databricks Workflows and PySpark focused on building scalable data solutions and analytics pipelines.

Benefits

Qualifications

• This hybrid role is for an experienced developer with strong
• validate solutions and align data products with strategic business objectives.
• Optimize PySpark jobs for performance and cost efficiency by tuning configurations managing partitioning strategies and leveraging best practices for large scale distributed processing.
• Ensure data quality and reliability by implementing validation checks error handling mechanisms and logging standards that reduce data issues and support faster troubleshooting.
• Conduct thorough unit testing integration testing and performance benchmarking for Databricks workflows to ensure stable releases and minimize disruptions to data consumers.
• Document datasets workflows and code logic in clear technical artifacts so that other team members can understand reuse and extend solutions effectively over time.
• Provide support for day shift operations by monitoring data workflows resolving production issues and proactively identifying opportunities to improve data reliability without requiring travel.
• Engage in continuous improvement by evaluating new Databricks features Python libraries and data engineering practices that can enhance scalability security and usability of data platforms.
• Collaborate with cloud and security teams to ensure that all Databricks development adheres to organizational policies governance standards and compliance
• benefiting stakeholders and society through responsible data use.
• Participate in code reviews and knowledge sharing sessions in the hybrid work environment to promote high quality development practices and foster a culture of continuous learning and innovation.
• Demonstrate strong proficiency in Python programming with hands on experience in writing efficient modular and testable code for data engineering and analytics use cases.

Responsibilities

• Design robust data processing pipelines using Python and PySpark in Databricks to transform complex data into reliable curated datasets that enable advanced analytics and reporting across the organization.
• Develop optimized Databricks SQL queries to support performant dashboards and analytical workloads ensuring data consumers can access trusted information with minimal latency and high reliability.
• Configure and manage Databricks Workflows to orchestrate end to end data jobs ensuring timely execution effective dependency handling and consistent delivery of data assets to downstream applications.
• Implement reusable modular code components and libraries in Python that standardize data transformations and business logic leading to faster development cycles and improved maintainability.
• Collaborate closely with data engineers analysts and product teams in a hybrid work model to gather

Skills

Degrees

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