Alteryx

Alteryx

• Primary skills:Technology->DataAnalytics->Alteryx Minimum Qualifications: • Bachelor’s degree (or equivalent) in Engineering/Technology/Computer Applications/Science (e.g., BTech, BE,MCA, MTech, MSc). • 2-5 years of experience working with Alteryx to build and maintain data preparation or automation workflows. • Strong hands-on experience in ETL and data workflow development, including transformation and validation steps. • Solid understanding of data concepts such as joins, aggregations, filtering, data types, and schema handling. • Ability to analyze requirements, debug workflow issues, and deliver reliable outputs with attention to detail. Preferred Qualifications: • Experience integrating Alteryx workflows with databases and file-based sources, including efficient input/output patterns. • Familiarity with SQL for data validation, reconciliation, and performance-aware querying alongside Alteryx pipelines. • Exposure to workflow scheduling/automation and operational support for recurring data processes. • Understanding of analytics lifecycle practices such as versioning, documentation standards, and reusable workflow design. • Ability to communicate clearly with stakeholders, present findings, and recommend improvements to data processes. Good to have skills: SQL, Data Warehousing, Data Quality Frameworks, Power BI, Python Key Responsibilities: • Design, build, and maintain Alteryx workflows for data preparation, transformation, and integration. • Develop and support ETL/data workflows to ingest, cleanse, validate, and enrich data from multiple sources. • Translate business requirements into technical workflow designs and reusable components/macros. • Implement data quality checks, reconciliation logic, and exception handling to ensure accuracy and completeness. • Optimize workflow performance by improving logic, reducing run time, and managing resource usage effectively. • Document workflow logic, data mappings, and operational steps to support maintainability and handovers. • Collaborate with analysts, data engineers, and stakeholders to troubleshoot issues and deliver enhancements. • Support production runs, monitor scheduled workflows, and resolve failures within defined timelines.