Overview

Responsibilities:
  • Implement automated data quality checks, validation rules, and reconciliation logic across data pipelines and DWH layers
  • Develop and maintain data quality monitoring dashboards, alerts, and exception-handling workflows
  • Support implementation of data quality frameworks, profiling routines, and anomaly detection checks
  • Collaborate with data engineers to embed data quality controls within ETL/ELT, CDC, and streaming pipelines
  • Work with business units, Data Owners, and analysts to translate business rules into technical validation logic
  • Participate in root-cause analysis of data quality issues and propose corrective actions
  • Document data quality logic, workflows, and metadata, ensuring traceability and lineage of checks
  • Assist in configuring and maintaining data governance and data quality tools
  • Contribute to promoting data quality automation and DataOps practices across the organization
Required Qualifications:
  • 2–4 years of experience in database development, BI/data analysis, data engineering, or data quality roles
  • Strong SQL skills and solid understanding of data warehouse concepts (fact/dimension tables, ETL flows, schemas)
  • Hands-on experience implementing data quality checks or validation rules
  • Experience with at least one data quality or governance tool, or the willingness to learn
  • Practical knowledge of Python or another scripting language for automation
  • Understanding of data lineage, metadata basics, and governance principles
  • Familiarity with data profiling methods, anomaly detection basics, or reconciliation techniques
  • Strong communication and collaboration skills to work with technical and business teams
Note:

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