QA/QC Data Validation Consultant

Job Title: QA/QC Data Validation Consultant

Job Location: (100% Remote)

Job Duration: 12 Months contract with a possibility of extension

Job Description:

• 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.

Proficiency in data analysis tools, statistical software, and databases.

• 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 techniques.
  • Knowledge of scheduling and monitoring ETL workflows.
  • Understanding of ELT processes, where transformations occur within the target system.
  • Experience with tools that support ELT, such as Snowflake and Databricks.
  • Proficiency in optimizing query performance for large-scale data transformations.
  • These skills will help ensure alignment with the technical requirements of these tools and platforms.