Module Lead - Snowflake, Python Job

8-10 Years

• JD: Data Engineer (Snowflake + Python+ visualization Tool) with Data Quality analysis
  If you are driven and want to make a difference with your problem-solving and data quality skills, adept at gathering requirements and working with key stakeholders and leaders to understand their DQ challenges, building complex DQ rules to accomplish the data quality thresholds needed by business.
  Have a keen sense of urgency in resolving the critical DQ issues and deploy a robust end to end remediation process.
  This role ultimately acts as a driving force behind building an industry leading DQ engine and remediation process to achieve data adoption and promote trusted data within Caterpillar Finance data platforms.
  The overall goal is to ensure the data quality meets the expectations needed for the business and that the data is governed and managed to meet brand, legal, information security & privacy, and document retention standards.
  Be an expert with data quality tools and industry practices and know how to incorporate advanced analytics into data quality and data management.
  Good requirement-gathering skills and experience as a technical business analyst.
  Know the DQ framework and approach in the context of financial data
  Good with documentation and creating process maps - for example - technical design documents, Requirement Docs, BRDs, Project Plan, Status Decks, etc.
  Good communication and relation-building skills to engage stakeholders.
  Tactile with a strategic vision to scale DQ solution.
  Go-getter and a problem solver.

  Technical Skills
  Snowflake and Snowpark: Strong proficiency in Snowpark for developing data processing pipelines and tasks.
  Python and SQL: Ability to write complex queries and scripts to automate data quality processes.
  Power BI:
  Alation Data Governance (Nice to Have): Alation
  Data Quality Frameworks and DQ Development:
  Strong understanding of data quality principles and experience implementing data quality frameworks.
  Deep knowledge of DQ dimensions and how to create reusable mapplets/rules for easy scalability
  Knowledge of data profiling, cleansing, and enrichment techniques.
  Expert at developing custom DQ rules using SQL/Python scripts, adept at building complex DQ engine to support business needs.
  Version Control: Git configuration changes
  Collaboration Tools: JIRA task tracking PM tools
  Database Skills: Solid understanding of relational DB and datalake warehouse concepts and experience working with various databases.
  Scripting and Automation:
  Troubleshooting and Optimization: