Description

You will own data quality processes in low-maturity environments, ensuring data is clean and ready for advanced analytics and AI. You will establish data quality frameworks from scratch, define metrics, and automate testing at scale.

This role is on-site.

Responsibilities

• Define and enforce data quality benchmarks for accuracy, completeness, timeliness, and consistency.
• Write complex SQL queries and Python scripts to validate data, perform transformations, and automate testing.
• Design and deploy automated data testing using observability platforms for real-time monitoring.
• Analyze data to identify anomalies, duplicates, and missing values; apply cleansing techniques to improve integrity.
• Collaborate with analytics and engineering teams to resolve root causes of data issues and support ML data readiness.

Required Skills

• 8-10 years of experience establishing data quality processes in environments with low data maturity.
• Advanced SQL proficiency for complex querying, validation, and transformation.
• Hands-on experience with ETL/ELT pipelines (e.g., SSIS) and data modeling principles.
• Proficiency in Python for data automation, validation, and pipeline integration (5-8 years).
• Experience with cloud data platforms (AWS, Azure, GCP) and modern data warehouses (Snowflake, BigQuery).
• Strong understanding of data profiling, cleansing, and data warehouse/lake/lakehouse architectures.
• Experience implementing data quality frameworks and metrics.
• Ability to navigate and improve data environments with low or immature data governance.

Preferred Skills

• Knowledge of DAMA-DMBoK, DCAM, MDM concepts, and governance frameworks.
• Experience with Microsoft Purview, Fabric, Power BI, and Key Vault.
• Familiarity with AI/ML data readiness and feature-store-aligned data structuring.

Key Skills

Education

Any Graduate