Data Validation QA / QC

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We're committed to bringing passion and customer focus to the business.

Fractal Analytics is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is one who empowers imagination with intelligence. And that it will be such Fractalites that will continue to build the company for the next 100 years.

Key Responsibilities 

• Data Validation: Analyze and validate data for accuracy, completeness, and consistency. 

• Quality Assurance: Develop and implement data quality standards and processes. 

• Issue Resolution: Identify and resolve data quality issues, ensuring compliance with regulations. 

• Collaboration: Work with IT and data management teams to design and implement data strategies. 

• Reporting: Generate data reports and presentations for management. 

Skills and Qualifications 

• Technical Skills: Proficiency in data analysis tools, statistical software, and databases. 

• To align technical skills with tools like Alteryx, Informatica IDMC, Snowflake, Databricks, ETL, and ELT, here are some specific skills and proficiencies you might focus on: 

• Alteryx 

• Proficiency in building workflows for data preparation, blending, and analysis. 

• Familiarity with Alteryx Designer for creating repeatable workflows. 

• Knowledge of Alteryx Server for deploying and managing workflows. 

• Experience with Alteryx Connect for metadata management. 

• Informatica IDMC (Intelligent Data Management Cloud) 

• Expertise in creating and managing data integration mappings. 

• Familiarity with Pushdown Optimization (PDO) for ELT processes. 

• Knowledge of hierarchical schema configuration and JSON data parsing. 

• Experience with cloud data integration and managing connections (e.g., AWS S3, Snowflake). 

• Snowflake 

• Understanding of Snowflake's architecture, including virtual warehouses and data sharing. 

• Proficiency in SQL for querying and managing data in Snowflake. 

• Experience with Snowflake's data integration capabilities, such as loading data from external sources. 

• Familiarity with Snowflake's security features, including role-based access control. 

• Databricks 

• Knowledge of Delta Lake for managing structured and unstructured data. 

• Proficiency in Apache Spark for big data processing. 

• Experience with Databricks notebooks for data engineering and machine learning tasks. 

• Familiarity with Databricks' integration with cloud platforms like Azure and AWS. 

• ETL (Extract, Transform, Load) 

• Expertise in designing and implementing ETL pipelines for data migration and transformation. 

• Familiarity with data cleansing and validation techniq