ENS Data Quality-Snowflake, SQL, and Python

Job Description

The ENS Data Quality Analyst is responsible for ensuring the accuracy, completeness, consistency, and traceability of data assets used across ENS analytics and AI initiatives.

This role plays a critical part in making enterprise data AIready by validating datasets, identifying quality issues, and working closely with data owners, engineers, and UX partners to ensure trustworthy outputs from downstream analytics and AI models.

The role requires strong hands on experience with modern data platforms and a disciplined approach to data governance, quality controls, and documentation.

Key Responsibilities Validate datasets transmitted, stored, and processed within Snowflake and/or Databricks to ensure they meet enterprise standards for completeness, accuracy, and consistency. Identify, analyse, and remediate data quality issues, including fundamental data cleaning and standardization activities.

Assess data readiness for AI and analytics use cases, proactively identifying and resolving quality issues that could impact model performance or decision making.

Document and validate end to end data lineage and traceability for ENS data sources ingested into Snowflake/Databricks.

Partner with UX engineers and downstream consumers to verify the correctness and reliability of AI model outputs and analytical results.

Design, develop, and deliver data quality reporting, metrics, and dashboards to provide transparency into data health.

Collaborate with data owners, engineers, and platform teams to drive root cause resolution of recurring data issues.

Contribute to the definition and continuous improvement of ENS data quality standards, controls, and best practices.

Required Skills and Experience:-

Strong hands on experience with Snowflake for data validation and analysis.

Experience working with Databricks, Kafka, and large scale data processing environments.

Proficiency in Python and SQL for data analysis, validation, and automation.

Experience using Git for version control and collaboration.

Familiarity with data visualization and monitoring tools such as Power BI and Grafana.

Solid background in Big Data Engineering and Data Analytics concepts.

Demonstrated experience working with complex, multisource enterprise datasets.

Preferred Qualifications:

Experience supporting AI or machine learning driven analytics use cases.

Strong understanding of data lineage, metadata management, and traceability in regulated environments.

Ability to communicate data quality issues clearly to both technical and non-technical stakeholders.