Position Details: Quality Consultant

Description:

Quality Consultants

Python (Use AI to make it fatser)

Data driven excerices, Data profiling, Vaildations , Buld the quality report,

Seeking a Data Quality Engineer / Data Management Consultant with intermediate expertise in data quality management, ETL development, Python, and SQL. The role will support data profiling, transformation, validation, remediation, and quality monitoring across enterprise datasets. Experience with Databricks is preferred.

 Key Responsibilities

 Design and execute data quality assessments to evaluate completeness, accuracy, consistency, validity, and uniqueness of enterprise data

 Support CDE determination and alignment for Tier 1 master data domains, including Customer, Material, Vendor, and Finance Master, in collaboration with business and data owners.

 Assess data quality across Tier 1 master data and selected Tier 2 master data objects, including BOM, Routing, PIR, Source List, Batch Master, and QM-based master data.

 Use Python and SQL to perform data profiling, validation, anomaly detection, reconciliation, and analysis of completeness, accuracy, consistency, validity, and uniqueness.

 Develop and support ETL/data processing workflows for data extraction, transformation, validation, and preparation of DQ assessment outputs across multiple ERP sources.

 Perform duplicate analysis and matching recommendations for Customer, Material/Product, and Vendor data across ECC instances and support cross-legacy to S/4 comparison scenarios.

 Partner with business SMEs, data owners, and technical teams to validate findings, identify root causes, and confirm remediation priorities.

Define and implement data quality rules, controls, and monitoring logic aligned to critical data elements and migration readiness requirements.

 Support development of a resource-loaded remediation plan covering data cleansing, enrichment, and construction activities.

 Identify and document data construction/fabrication scenarios and contribute to the data cleansing, enrichment, and construction architecture for migration.

 Facilitate preparation of DQ assessment reports, deduplication reports, executive sprint readouts, and supporting documentation.

 Required Qualifications

 Experience in Data Management with working knowledge of:

 Data Quality Management Advanced level

 ETL Architecture & Development Advanced level

 Python – Data Manipulation & Analysis Advanced level

 Standard SQL Advanced level

 Strong understanding of data profiling, validation, transformation, and reconciliation techniques

 Experience identifying data defects and translating them into actionable remediation steps

 Ability to work across structured datasets and support data quality improvement initiatives

Strong analytical, problem-solving, and communication skills

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